Land resource surveying and mapping collaborative operation processing method and system

Abstract

The invention discloses a territorial resource surveying and mapping collaborative operation processing method and system, and relates to the technical field of resource surveying and mapping processing, and the technical scheme is characterized in that the method comprises the following steps: constructing a collaborative relation network of territorial resource surveying and mapping operation participants, extracting collaborative interaction behavior data of each main body in a collaborative operation process and multi-dimensional quality parameters in surveying and mapping results from the collaborative relation network; calculating the quality volatility of each surveying and mapping task in different links of the collaborative operation based on the collaborative interaction behavior data and the quality parameters, and generating a task quality volatility data set; collaborative scene categories related to the surveying and mapping tasks are identified, and quality influence transfer coefficients among the surveying and mapping tasks under different collaborative scene categories are judged; the method has the advantages that collaborative internal friction and flow jamming are reduced, full-chain efficient operation of surveying and mapping tasks from quality analysis to optimization processing is achieved, and the overall operation efficiency and comprehensive benefits of land resource surveying and mapping collaborative operation are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of resource surveying and mapping technology, and more specifically, to a collaborative operation processing method and system for land and resources surveying and mapping. Background Technology

[0002] Collaborative land and resources surveying and mapping operations involve multiple stakeholders, including surveying and mapping implementation, data provision, quality verification, technical support, and results application. The operation process is complex and involves many steps. The coordination and quality control among the stakeholders directly affect the accuracy and application value of the surveying and mapping results.

[0003] However, existing collaborative processing methods for land and resources surveying and mapping do not clearly distinguish between core and optimization categories of collaborative scenarios, nor do they establish a scientific system for calculating correction time. This results in a lack of clear priorities and time planning for quality correction work, and a low degree of matching between participating entities, operation periods, and correction tasks. This often leads to unreasonable resource allocation, duplicate inputs, or resource shortages, causing not only resource waste but also low efficiency in quality correction, untimely rectification of quality problems, and impacting the overall work progress. Furthermore, the lack of effective standards for the collaboration models of participating entities, unclear interaction processes, and ambiguous division of operational permissions result in severe internal friction, chaotic work rhythms, and frequent process bottlenecks. The entire chain of surveying and mapping tasks, from quality analysis to optimization, is not running smoothly, leading to low overall operational efficiency. It is difficult to simultaneously improve the stability of surveying and mapping results and operational efficiency, and it cannot fully meet the urgent needs of land and resources management, planning, and other fields for high-quality surveying and mapping data. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method and system for collaborative operation processing of land and resources surveying and mapping.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for collaborative land and resources surveying and mapping, comprising the following steps: Construct a collaborative relationship network among the main participants in land and resources surveying and mapping operations, and extract collaborative interaction data of each participant in the collaborative operation process and multi-dimensional quality parameters from the surveying and mapping results from the collaborative relationship network; Based on the collaborative interaction behavior data and quality parameters, the quality fluctuation rate of each surveying and mapping task at different stages of collaborative operation is calculated to generate a task quality fluctuation rate dataset. Identify the collaborative scenario categories involved in the surveying and mapping tasks, determine the quality impact transmission coefficients between surveying and mapping tasks under different collaborative scenario categories, and obtain the task quality impact transmission coefficient set; The core collaborative scenario category data is determined by the collaborative scenario category, the maximum quality deviation value of each surveying and mapping task in the core collaborative scenario is extracted, and the benchmark quality volatility is selected from the task quality volatility dataset. Based on the core collaborative scenario category data and the benchmark quality volatility, determine the initial correction time required to correct the maximum quality deviation value to the acceptable quality range; The collaborative scenario category data to be optimized is determined by the collaborative scenario category, the associated quality impact data is judged by the task quality impact transmission coefficient set, and the secondary correction time required to correct the associated quality impact data to the qualified quality range is determined by combining the collaborative scenario category data to be optimized and the associated quality impact data. Based on the primary correction time, secondary correction time, and processing priority of collaborative scenarios, collaborative quality optimization processing is carried out on all surveying and mapping tasks and participating entities to obtain the collaborative operation processing results of land and resources surveying and mapping.

[0006] Preferably, constructing a collaborative relationship network among the main participants in land and resources surveying and mapping operations includes the following steps: The types of participating entities are determined, including surveying and mapping implementation entities, data providers, quality verification entities, technical support entities, and results application entities, and identity identifiers are assigned to each participating entity. Collect business interaction information between various entities and categorize interactions based on the type of interaction content. A subject association matrix is ​​established based on identity. Each row in the matrix corresponds to the initiating subject, and each column corresponds to the receiving subject. The matrix element values ​​represent the interaction frequency between the initiating subject and the receiving subject. The association relationship level is obtained by classifying the interaction frequency. Construct a collaborative relationship network by combining subject type, interaction category, and association level.

[0007] Preferably, the collaborative interaction data of each subject during the collaborative operation process and the multi-dimensional quality parameters in the surveying and mapping results are extracted from the collaborative relationship network, specifically including the following steps: Based on the subject associations represented by nodes and connections in the collaborative relationship network, we capture the triggering conditions when each subject initiates an interaction, the interval between initiation and response, the integrity status of data transmission, task stage identifiers, and result attributes to obtain collaborative interaction behavior data. Based on the terrain type, regional characteristics, and application scenarios of the results involved in the collaborative operation, the basic quality dimension and the extended quality dimension are determined. Feature extraction rules are set for each quality dimension. By comparing the basic quality with the extended quality, the specific parameter values ​​under each dimension are obtained to obtain multi-dimensional quality parameters.

[0008] Preferably, based on the collaborative interaction behavior data and quality parameters, the quality volatility of each surveying and mapping task at different stages of collaborative operation is calculated to generate a task quality volatility dataset, specifically including the following steps: The collaborative operation of the surveying and mapping task is divided into stages. For each stage, the response time deviation value and data transmission integrity fluctuation value are extracted from the collaborative interaction behavior data of the corresponding stage. Combined with the key indicators in the quality parameters of each stage, the response time deviation value, data transmission integrity fluctuation value and key indicators of each stage are assigned influence weights respectively. The quality fluctuation index of a given stage is obtained by calculating the values ​​of each parameter in the same stage and their corresponding influence weights. Calculate the difference in quality fluctuation index between two adjacent stages, and compare the difference in quality fluctuation index with the quality fluctuation index to obtain the quality fluctuation transmission rate between stages; By combining the quality fluctuation index of each stage with the quality fluctuation transfer rate between stages, a comprehensive quality fluctuation rate for each surveying and mapping task at different stages is generated. The overall quality volatility of all surveying and mapping tasks at each stage is summarized and categorized according to task identifier and stage order to form a task quality volatility dataset.

[0009] Preferably, the process involves identifying the collaborative scenario categories involved in the surveying and mapping task, determining the quality impact transmission coefficients between surveying and mapping tasks under different collaborative scenario categories, and obtaining a set of task quality impact transmission coefficients. This specifically includes the following steps: Based on the regional span of the surveying and mapping task, the number of participating entities, and the type of technology integration, collaborative scenario categories are divided. For each collaborative scenario category, the correlation features of all surveying and mapping tasks within the scenario are extracted, and a task correlation map within the scenario is constructed based on the correlation features. Extract the peak value of quality fluctuation in the preceding task and the response value of quality deviation in the subsequent task, and calculate the ratio of the response value of quality deviation to the peak value of quality fluctuation to obtain the basic influence coefficient; Based on the degree of correlation between tasks within the correlation graph, the basic influence coefficient is corrected, and the corrected basic influence coefficient is used as the quality influence transfer coefficient of the task pair. The quality impact transmission coefficients of all task pairs under the same collaborative scenario category are summarized, and then organized according to the scenario category and the direction of task association to form a sub-coefficient set for each scenario category. The sub-coefficient sets of all scenario categories are integrated to obtain the task quality impact transmission coefficient set.

[0010] Preferably, the core collaborative scenario category data is determined by the collaborative scenario category, the maximum quality deviation value of each surveying and mapping task in the core collaborative scenario is extracted, and the benchmark quality volatility is selected from the task quality volatility dataset. Specifically, this includes the following steps: The weights of collaborative scenario categories are determined by the collaborative scenario categories. The scenario categories with the highest weights are selected as the core collaborative scenario categories, and the scenario feature data under these categories are extracted to form the core collaborative scenario category data. By comparing the actual values ​​of quality parameters of each work step with the corresponding quality standard values ​​of the core collaborative scenario category data, the deviation is calculated, and the maximum quality deviation value is obtained by judging the maximum value of the deviation as the work progresses from the standard value. Select the quality volatility that meets the preset conditions from the task quality volatility dataset as the benchmark quality volatility; The selected quality volatility rates are grouped according to the core collaborative scenario categories, and the quality volatility rate with the highest frequency in each group is taken as the benchmark quality volatility rate for that category.

[0011] Preferably, based on the core collaborative scenario category data and the benchmark quality volatility, the initial correction time required to correct the maximum quality deviation value to the acceptable quality range is determined, specifically including the following steps: Extract key nodes for quality verification and standard processes for interaction between subjects from the core collaborative scenario category data, and determine the collaborative subjects involved in the correction work and the operation permissions of each subject; The deviation of the maximum quality deviation value from the benchmark state is calculated based on the benchmark quality volatility. The basic correction duration is determined by combining the deviation range with the standard response time of the main interaction in the core collaborative scenario. Based on the work process corresponding to the maximum quality deviation value, the number of associated entities in the core collaborative scenario of that process is called, and the basic correction time is adjusted in a stepwise manner according to the number of associated entities. When an additional associated entity is added, the corresponding proportion of coordination time is added to the basic correction time. Determine the verification levels that need to be passed during the correction process. When the verification level increases, the primary correction time is obtained by coordinating the correction time adjustment with the corresponding verification standard time of that level.

[0012] Preferably, the collaborative scenario category data to be optimized is determined by the collaborative scenario category, the associated quality impact data is judged by the task quality impact propagation coefficient set, and the secondary correction time required to correct the associated quality impact data to the qualified quality range is determined by combining the collaborative scenario category data to be optimized and the associated quality impact data. Specifically, this includes the following steps: From the collaborative scenario categories, select scenario categories that are ranked after the core collaborative scenario categories in terms of weight as collaborative scenario categories to be optimized, extract the scenario operation characteristics under this category, and form collaborative scenario category data to be optimized; Based on the task quality impact transmission coefficient set, filter the sub-coefficient set corresponding to the collaborative scenario category to be optimized, determine the associated tasks affected by the maximum quality deviation value in the core collaborative scenario, and obtain the associated quality impact data by calculating the deviation between the actual value of the quality parameter of the associated task and the qualified range. Extract non-standard interaction processes of main entities from the collaborative scenario category data to be optimized, and determine the response patterns of main entities involved in correcting the data that affects the quality of related data; Based on the response pattern and the degree of deviation of the associated quality impact data, the simplified node for quality verification is obtained by judging the category of collaborative scenario data to be optimized. The quality verification simplifies the nodes and determines the verification steps that the correction process needs to go through. The secondary correction duration is obtained by calculating the preset verification time corresponding to each verification step.

[0013] Preferably, based on the primary correction duration, secondary correction duration, and processing priority of collaborative scenarios, collaborative quality optimization processing is carried out on all surveying and mapping tasks and participating entities to obtain the collaborative operation processing results for land and resources surveying and mapping. Specifically, this includes the following steps: In collaborative scenarios, scenarios of the same priority are sorted from shortest to longest correction time to form an optimized processing sequence; The operation time period of each participating entity is allocated according to the primary correction time in the optimization processing sequence, and the correction operation content of the entity in the corresponding time period is clarified. Based on the duration of secondary corrections and the response patterns within the scenario, the correction rhythm of related tasks is statistically analyzed. Based on the comparison between the correction operation content and correction rhythm and the preset correction duration, the correction data of related scenarios are updated synchronously. When the quality parameters of all surveying and mapping tasks have entered the qualified range, the optimization processing records of each scenario are summarized to form the collaborative operation processing results of land and resources surveying and mapping.

[0014] A collaborative land and resources surveying and mapping system includes the following steps: Data extraction module: Construct a collaborative relationship network of the main participants in land and resources surveying and mapping operations, and extract collaborative interaction data of each main participant in the collaborative operation process and multi-dimensional quality parameters from the surveying and mapping results from the collaborative relationship network; Data generation module: Based on the collaborative interaction behavior data and quality parameters, calculate the quality volatility of each surveying and mapping task at different stages of collaborative operation, and generate a task quality volatility dataset; Identification module: Identifies the collaborative scenario categories involved in the surveying and mapping task, determines the quality impact transmission coefficient between surveying and mapping tasks under different collaborative scenario categories, and obtains the task quality impact transmission coefficient set; Extraction module: Determines core collaborative scenario category data by collaborative scenario category, extracts the maximum quality deviation value of each surveying and mapping task in the core collaborative scenario, and filters the benchmark quality volatility from the task quality volatility dataset; Judgment Module 1: Based on the core collaborative scenario category data and the benchmark quality volatility, determine the initial correction time required to correct the maximum quality deviation value to the acceptable quality range; Judgment Module 2: Determine the collaborative scenario category data to be optimized by the collaborative scenario category, use the task quality impact transmission coefficient set to judge the associated quality impact data, and combine the collaborative scenario category data to be optimized and the associated quality impact data to determine the secondary correction time required to correct the associated quality impact data to the qualified quality range. Output module: Based on the primary correction time, secondary correction time and the processing priority of collaborative scenarios, collaborative quality optimization processing is carried out on all surveying and mapping tasks and participating entities to obtain the processing results of land and resources surveying and mapping collaborative operations.

[0015] Compared with existing technologies, this invention has the following beneficial effects: By constructing a collaborative relationship network and extracting collaborative interaction behavior data and multi-dimensional quality parameters, it achieves accurate capture of quality influencing factors throughout the entire surveying and mapping operation process. Combined with quality fluctuation rate calculation and quality impact transmission coefficient analysis, it can clearly locate the quality weaknesses and impact transmission paths of each task in different collaborative scenarios, providing targeted basis for quality control and effectively reducing quality risks. By distinguishing between core and optimization-needed collaborative scenarios and calculating the primary and secondary correction times respectively, it clarifies the priority and time planning of quality correction, achieving precise matching of participating entities, operation periods, and correction tasks, avoiding resource waste, significantly improving quality correction efficiency, ensuring rapid rectification of quality problems, optimizing the collaboration mode and operation rhythm of each participating entity, reducing collaborative internal friction and process bottlenecks, and realizing efficient operation of the entire chain of surveying and mapping tasks from quality analysis to optimization processing, effectively improving the overall operational efficiency and comprehensive benefits of land and resources surveying and mapping collaborative operations. Attached Figure Description

[0016] Figure 1 This invention provides a schematic diagram illustrating the steps of a collaborative land and resources surveying and mapping processing method. Figure 2 This is a schematic diagram illustrating the steps involved in generating a task quality fluctuation rate dataset in a collaborative land and resources surveying and mapping processing method proposed in this invention. Figure 3 This invention presents a schematic diagram of a collaborative operation processing system for land and resources surveying. Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0020] Reference Figures 1-3 As shown.

[0021] Example 1 further illustrates the collaborative operation processing method and system for land and resources surveying proposed in this invention.

[0022] A method for collaborative land and resources surveying and mapping, comprising the following steps: Construct a collaborative relationship network among the main participants in land and resources surveying and mapping operations, and extract collaborative interaction data of each participant in the collaborative operation process and multi-dimensional quality parameters from the surveying and mapping results from the collaborative relationship network; Based on the collaborative interaction behavior data and quality parameters, the quality fluctuation rate of each surveying and mapping task at different stages of collaborative operation is calculated to generate a task quality fluctuation rate dataset. Identify the collaborative scenario categories involved in the surveying and mapping tasks, determine the quality impact transmission coefficients between surveying and mapping tasks under different collaborative scenario categories, and obtain the task quality impact transmission coefficient set; The core collaborative scenario category data is determined by the collaborative scenario category, the maximum quality deviation value of each surveying and mapping task in the core collaborative scenario is extracted, and the benchmark quality volatility is selected from the task quality volatility dataset. Based on the core collaborative scenario category data and the benchmark quality volatility, determine the initial correction time required to correct the maximum quality deviation value to the acceptable quality range; The collaborative scenario category data to be optimized is determined by the collaborative scenario category, the associated quality impact data is judged by the task quality impact transmission coefficient set, and the secondary correction time required to correct the associated quality impact data to the qualified quality range is determined by combining the collaborative scenario category data to be optimized and the associated quality impact data. Based on the primary correction time, secondary correction time, and processing priority of collaborative scenarios, collaborative quality optimization processing is carried out on all surveying and mapping tasks and participating entities to obtain the collaborative operation processing results of land and resources surveying and mapping.

[0023] A collaborative relationship network is constructed among the participating entities in land and resources surveying and mapping operations. These entities include surveying and mapping execution units, quality supervision agencies, and data processing departments. Key data is extracted from the network, such as collaborative interaction data of each entity during collaborative operations. This includes information reflecting the collaborative status, such as communication frequency, task coordination efficiency, and timeliness of information sharing. Multi-dimensional quality parameters in the surveying and mapping results, including indicators that directly reflect the surveying and mapping quality, such as data accuracy, error range, and result completeness, provide basic data support for subsequent quality analysis. For each surveying and mapping task, the quality fluctuation rate is calculated in different stages of collaborative operation, such as data acquisition, data processing, and result review. The calculation of quality fluctuation rate needs to take into account the changes in quality parameters in each stage and the impact of collaborative interaction on quality. The degree of quality fluctuation of each task in each stage is obtained through quantitative analysis, and then integrated to generate a task quality fluctuation rate dataset, which intuitively reflects the quality stability of each task in the collaborative process. Since the impact mechanisms on task quality differ in different collaborative scenarios, such as cross-regional collaborative mapping, multi-technology integrated collaborative mapping, and multi-department joint review, it is necessary to further determine the quality impact transmission coefficient between mapping tasks under different collaborative scenario categories. This coefficient is used to measure the degree to which the quality fluctuation of one task affects the quality of other related tasks. By analyzing the correlation between tasks and scenario characteristics, a set of task quality impact transmission coefficients is formed, providing a basis for subsequent quality impact analysis. Extract the maximum quality deviation value of each surveying task, that is, the maximum deviation of the quality parameters of the task in the core scene from the qualified standard; at the same time, select the benchmark quality volatility from the task quality volatility dataset as a reference standard for judging the quality correction needs. The determination of the benchmark value needs to be combined with industry quality standards and historical high-quality operation data. The calculation of the initial correction time should take into account the complexity of the core scenario, the severity of the quality deviation, the technical means and resource allocation required for correction, so as to ensure that the estimation of the correction time is scientific and reasonable and to provide support for the time planning of subsequent correction work. For the collaborative scenario category data to be optimized, which is a scenario that has a certain impact on quality but is not core, the task quality impact propagation coefficient set is used to determine the associated quality impact data. That is, the quality fluctuations of each task in the scenario to be optimized affect other tasks through the impact propagation. Combining the characteristics of the collaborative scenario category data to be optimized and the severity of the associated quality impact data, the secondary correction time required to correct the associated quality impact data to the qualified quality range is further determined. The secondary correction time needs to be accurately estimated based on factors such as the processing difficulty of the scenario to be optimized and the scope of the associated impact. Based on the primary correction time, secondary correction time, and processing priority of collaborative scenarios, a collaborative quality optimization processing plan is formulated. The optimization responsibilities, work content, and time nodes of each surveying and mapping task and participating entity are clarified. By coordinating the work of each participating entity, quality correction work is carried out for core scenarios and scenarios to be optimized, ensuring that the quality of all surveying and mapping tasks meets the qualified standards. Finally, the collaborative operation processing results of land and resources surveying and mapping are obtained, and the overall optimization and improvement of collaborative operation quality is achieved.

[0024] Constructing a collaborative network among the main participants in land and resources surveying and mapping operations includes the following steps: The types of participating entities are determined, including surveying and mapping implementation entities, data providers, quality verification entities, technical support entities, and results application entities, and identity identifiers are assigned to each participating entity. Collect business interaction information between various entities and categorize interactions based on the type of interaction content. A subject association matrix is ​​established based on identity. Each row in the matrix corresponds to the initiating subject, and each column corresponds to the receiving subject. The matrix element values ​​represent the interaction frequency between the initiating subject and the receiving subject. The association relationship level is obtained by classifying the interaction frequency. Construct a collaborative relationship network by combining subject type, interaction category, and association level.

[0025] After determining the types of participating entities, an identity identifier is assigned to each participating entity to ensure that each entity can be accurately identified in subsequent information processing and correlation analysis, avoiding entity confusion. For example, the surveying and mapping institute, as the surveying and mapping implementation entity, can be assigned the identity identifier C1001, and the geographic information data center, as the data provider entity, can be assigned the identity identifier D2001. Interactive information is categorized based on the specific type of interactive content. Different interactive content corresponds to different interactive categories. For example, when a surveying and mapping entity applies for basic terrain data from a data provider, it can be classified as a data acquisition interaction; when a quality verification entity conducts quality inspection on the surveying and mapping results submitted by the surveying and mapping entity and provides feedback, it can be classified as a quality review interaction; and when a technical support entity provides technical guidance on the debugging of surveying and mapping equipment to the surveying and mapping entity, it can be classified as a technical support interaction. A subject association matrix is ​​established based on identity identifiers. In the matrix, each row corresponds to a subject that initiates an interaction, and each column corresponds to a subject that receives an interaction. The element values ​​in the matrix represent the frequency of interaction between the initiating and receiving subjects. For example, if a surveying and mapping implementation subject with identity identifier C1001 initiates 15 data acquisition interactions with a data provider subject with identity identifier D2001, the element value in the matrix corresponding to the row containing C1001 and the column containing D2001 will be 15. The interaction frequency between all subjects is classified into levels, and different level standards are set according to the frequency to determine the level of association between each subject. For example, an interaction frequency of 0-5 times is set as a weak association level, 6-15 times as a medium association level, and 16 times or more as a strong association level. Therefore, the association level between C1001 and D2001 is a medium association level. By combining the subject type, the classified interaction category, and the assessed association level, a complete collaborative relationship network is constructed. In the network, each node represents a participating subject with a specific type and unique identity. The lines between nodes represent the association between subjects, and the attributes of the lines will indicate the corresponding interaction category and association level. Through this network structure, the division of labor, interaction content, and degree of association among the participating subjects in land and resources surveying and mapping operations can be clearly and intuitively displayed, laying a solid foundation for further analysis of the collaborative interaction behavior between subjects and the factors affecting surveying and mapping quality.

[0026] Extracting collaborative interaction data of each entity during collaborative operations and multi-dimensional quality parameters from surveying and mapping results from the collaborative relationship network includes the following steps: Based on the subject associations represented by nodes and connections in the collaborative relationship network, we capture the triggering conditions when each subject initiates an interaction, the interval between initiation and response, the integrity status of data transmission, task stage identifiers, and result attributes to obtain collaborative interaction behavior data. Based on the terrain type, regional characteristics, and application scenarios of the results involved in the collaborative operation, the basic quality dimension and the extended quality dimension are determined. Feature extraction rules are set for each quality dimension. By comparing the basic quality with the extended quality, the specific parameter values ​​under each dimension are obtained to obtain multi-dimensional quality parameters.

[0027] The system comprehensively captures key information when various entities initiate interactions, thereby forming collaborative interaction behavior data. It records the triggering conditions for interactions, such as a surveying entity needing basic elevation data for a mountain topographic survey and thus initiating a data request to the data provider, thus triggering the interaction. Simultaneously, it tracks the interval between the initiation of the interaction and the receiving entity's response. For example, if the surveying entity initiates a data request at 10:00 AM and the data provider responds at 10:20 AM, the interaction interval is 20 minutes. It verifies the integrity of data transmission; for instance, if the data provider needs to transmit 100 sets of topographic coordinate data but only successfully transmits 98 sets, leaving two sets missing, the data transmission integrity is considered incomplete. It clearly identifies the corresponding task stage of the interaction; for example, if the data request belongs to the data acquisition stage in a surveying operation, the corresponding stage identifier is marked. Finally, it records the result attributes of the interaction. If the data provider successfully supplements the missing data, meeting the surveying entity's needs, the result attribute is a valid interaction; if the data provider fails to supplement the data, resulting in unmet needs, the result attribute is an invalid interaction. Basic quality dimensions are core quality indicators that all types of surveying and mapping operations need to focus on, while extended quality dimensions are supplemented according to the specific characteristics of the operation. For example, when collaborative operations involve plains or urban areas and the results are used for urban planning, basic quality dimensions may include data accuracy, result completeness, and error control range, while extended quality dimensions may include data timeliness and result visualization clarity. If the operation involves plateau terrain or remote mountainous areas and the results are used for resource exploration, basic quality dimensions also cover data accuracy, result completeness, and error control range, while extended quality dimensions may be set as data stability under extreme environments and the geological information correlation of the results, in order to adapt to the characteristics of operations in plateau and remote areas and the application needs of resource exploration results.

[0028] By comparing the basic and extended quality of actual surveying and mapping results according to rules, specific parameter values ​​for each dimension are obtained, forming multi-dimensional quality parameters. Taking the basic quality dimension of data accuracy as an example, the feature extraction rule is set as follows: the deviation between the actual measured data and the standard benchmark data should not exceed 0.5 meters. If the data of fifty measurement points in the surveying and mapping results are compared, it is found that the deviation of forty-five measurement points is within 0.3 meters, and the deviation of five measurement points is between 0.3 and 0.45 meters, all of which do not exceed the set threshold. Thus, the parameter value of the data accuracy dimension is obtained as an average deviation of 0.32 meters. For the data timeliness in the extended quality dimension, the extraction rule is set as follows: the time interval between the collection time and the submission time of the result data should not exceed seven days. If the result data collection time is the first day of the month and the submission time is the sixth day of the month, the interval is five days, which meets the rule requirements. Then, the parameter value of the data timeliness dimension is an interval of five days. By extracting and comparing features for each of the basic and extended quality dimensions, a comprehensive multi-dimensional quality parameter is finally obtained.

[0029] Based on the collaborative interaction behavior data and quality parameters, the quality volatility of each surveying task at different stages of collaborative operation is calculated to generate a task quality volatility dataset, specifically including the following steps: The collaborative operation of the surveying and mapping task is divided into stages. For each stage, the response time deviation value and data transmission integrity fluctuation value are extracted from the collaborative interaction behavior data of the corresponding stage. Combined with the key indicators in the quality parameters of each stage, the response time deviation value, data transmission integrity fluctuation value and key indicators of each stage are assigned influence weights respectively. The quality fluctuation index of a given stage is obtained by calculating the values ​​of each parameter in the same stage and their corresponding influence weights. Calculate the difference in quality fluctuation index between two adjacent stages, and compare the difference in quality fluctuation index with the quality fluctuation index to obtain the quality fluctuation transmission rate between stages; By combining the quality fluctuation index of each stage with the quality fluctuation transfer rate between stages, a comprehensive quality fluctuation rate for each surveying and mapping task at different stages is generated. The overall quality volatility of all surveying and mapping tasks at each stage is summarized and categorized according to task identifier and stage order to form a task quality volatility dataset.

[0030] The collaborative work processes of surveying and mapping tasks are clearly divided into key stages: data acquisition, data processing, results review, and results delivery. After this division, for each stage, two core indicators are extracted from the corresponding collaborative interaction data: response timeliness deviation and data transmission integrity fluctuation. Response timeliness deviation refers to the difference between the actual response time and the standard response time for that stage, while data transmission integrity fluctuation is the fluctuation range between the actual value and the standard value of data transmission integrity for that stage. Key indicators are also extracted from the quality parameters of each stage; for example, the key quality indicator for the data acquisition stage is data measurement accuracy, and for the data processing stage... The key quality indicator is the data correction accuracy rate, and the key quality indicator for the results review stage is the problem detection rate. Based on the degree of influence of each indicator on the quality of each stage, the response time deviation value, the data transmission integrity fluctuation value, and the key quality indicator are assigned influence weights respectively. For example, in the data acquisition stage, the data measurement accuracy has the greatest impact on quality and is assigned a weight of 0.5, the response time deviation value has the second greatest impact and is assigned a weight of 0.3, and the data transmission integrity fluctuation value is assigned a weight of 0.2. In the results review stage, the problem detection rate has a weight of 0.6, the response time deviation value has a weight of 0.2, and the data transmission integrity fluctuation value has a weight of 0.2. The extracted parameter values ​​are weighted and calculated with their corresponding influence weights to obtain the quality fluctuation index for this stage. For example, in the data acquisition stage, the task response time deviation is 10, the standard value is 5, and the actual value is 15; the data transmission integrity fluctuation is 8, the standard value is 95, and the actual value is 87, with a fluctuation amplitude of 8; and the data measurement accuracy parameter is 6, the standard value is 10, the actual value is 4, and the deviation is 6. According to the weights, the quality fluctuation index for this stage is 10×0.3+8×0.2+6×0.5=3+1.6+3=7.6. Calculate the difference in quality fluctuation index between two adjacent stages and compare this difference with the quality fluctuation index of the preceding stage to obtain the quality fluctuation transmission rate between stages. This reflects the degree of influence of quality fluctuation in the preceding stage on the following stage. For example, if the quality fluctuation index of the data acquisition stage is 7.6 and the quality fluctuation index of the data processing stage is 6.1, the difference between the two is 1.5. Then the quality fluctuation transmission rate from data acquisition to data processing stage is 1.5 ÷ 7.6 ≈ 0.197, or about 19.7%, indicating that about 19.7% of the quality fluctuation in the data acquisition stage is transmitted to the data processing stage. The comprehensive quality volatility of each surveying and mapping task at different stages is generated through comprehensive calculation. The comprehensive quality volatility not only reflects the quality volatility of this stage, but also takes into account the transmission effect of the quality volatility of the previous stage. For example, the comprehensive quality volatility of the data processing stage can be calculated by adding the quality volatility index of this stage to the volatility part transmitted from the previous stage, that is, 6.1+7.6×0.197≈6.1+1.5=7.6. The overall quality volatility of all surveying and mapping tasks at each stage is summarized and categorized according to the unique identifier of each task and the order of the operational stages. For example, the overall quality volatility of Task 1 is arranged in the order of data collection, data processing, result review, and result delivery. Task 2 is also arranged in the same order. Finally, a complete dataset of task quality volatility is formed, which provides data support for subsequent analysis of the stability of task quality and optimization decisions.

[0031] Identify the collaborative scenario categories involved in the surveying and mapping tasks, determine the quality impact transmission coefficients between surveying and mapping tasks under different collaborative scenario categories, and obtain the task quality impact transmission coefficient set. This process includes the following steps: Based on the regional span of the surveying and mapping task, the number of participating entities, and the type of technology integration, collaborative scenario categories are divided. For each collaborative scenario category, the correlation features of all surveying and mapping tasks within the scenario are extracted, and a task correlation map within the scenario is constructed based on the correlation features. Extract the peak value of quality fluctuation in the preceding task and the response value of quality deviation in the subsequent task, and calculate the ratio of the response value of quality deviation to the peak value of quality fluctuation to obtain the basic influence coefficient; Based on the degree of correlation between tasks within the correlation graph, the basic influence coefficient is corrected, and the corrected basic influence coefficient is used as the quality influence transfer coefficient of the task pair. The quality impact transmission coefficients of all task pairs under the same collaborative scenario category are summarized, and then organized according to the scenario category and the direction of task association to form a sub-coefficient set for each scenario category. The sub-coefficient sets of all scenario categories are integrated to obtain the task quality impact transmission coefficient set.

[0032] Based on the core characteristics of surveying and mapping tasks, collaborative scenarios are categorized, primarily considering three key dimensions: regional span, number of participating entities, and type of technology integration. Regional span can be divided into single-region, cross-regional, and nationwide types. The number of participating entities can be divided into a small number of entities, medium-sized entities, and large-scale entity collaboration. The type of technology integration can be divided into single technology application, dual-technology integration, and multi-technology comprehensive application. For example, a surveying and mapping task covering multiple provinces is considered cross-regional, involving ten participating entities is considered medium-sized entity collaboration, and integrating remote sensing and GIS technologies is considered dual-technology integration. Combining these three factors, it can be classified as a cross-regional, medium-sized, dual-technology integration collaborative scenario. After classification, for each collaborative scenario category, the correlation features of all surveying and mapping tasks within the scenario are extracted, including the business connection relationship between tasks, the degree of data sharing dependence, and the progress coordination requirements. Based on the correlation features, a task correlation map within the scenario is constructed. In the map, nodes represent surveying and mapping tasks, and lines represent the correlation relationships between tasks. The thickness of the lines can intuitively reflect the tightness of the correlation. Extract the peak value of quality fluctuation during the operation of the preceding task. This peak value is the maximum deviation of the quality parameter of the preceding task from the standard value. For example, if the standard value of the data measurement accuracy of the preceding task is ±0.3 meters and the actual maximum deviation is 0.6 meters, then its peak value of quality fluctuation is 0.6 meters. At the same time, extract the quality deviation response value of the subsequent task caused by the influence of the preceding task, that is, the quality deviation value of the subsequent task due to the quality fluctuation of the preceding task. If the accuracy deviation of 0.6 meters in the preceding task causes a deviation of 0.4 meters in the data processing of the subsequent task, then the quality deviation response value of the subsequent task is 0.4 meters. Compare the quality deviation response value of the subsequent task with the peak value of quality fluctuation of the preceding task to obtain the basic influence coefficient. The basic influence coefficient of the above task pair is 0.4 ÷ 0.6 ≈ 0.67. The basic influence coefficient is adjusted based on the degree of task association in the task association graph. The degree of association can be comprehensively evaluated by the frequency of interaction between tasks, the frequency of data transmission, and the degree of cooperation. It is divided into three levels: close, medium, and loose. Different levels correspond to different adjustment coefficients. For example, the close association level corresponds to a adjustment coefficient of 1.2, the medium level corresponds to 1.0, and the loose level corresponds to 0.8. If the above task pair has a thicker connection in the association graph and belongs to the close association level, then the adjusted quality influence transmission coefficient is 0.67×1.2≈0.80. If another task pair has a basic influence coefficient of 0.5 and a loose association level, its adjusted transmission coefficient is 0.5×0.8=0.40. The adjustment process ensures that the coefficient can simultaneously reflect the degree of quality influence and the strength of task association. The quality impact transmission coefficients of all task pairs within the same collaborative scenario category are summarized and organized according to the scenario category and task association direction. The scenario category serves as the primary classification, and each scenario category is further subdivided according to the task association direction, forming a sub-coefficient set for each scenario category. For example, in cross-regional, medium-scale, and dual-technology integrated scenarios, the transmission coefficients of all preceding and corresponding subsequent tasks are compiled to form a sub-coefficient set for that scenario. Similarly, in single-regional, small-scale, and single-technology application scenarios, independent sub-coefficient sets are also formed. The sub-coefficient sets of all scenario categories are then integrated, duplicate data is removed, and the format is standardized to obtain a complete set of task quality impact transmission coefficients, providing data support for subsequent analysis of the transmission patterns of task quality impact in different scenarios.

[0033] The core collaborative scenario category data is determined by classifying collaborative scenarios. The maximum quality deviation value of each surveying and mapping task in the core collaborative scenario is extracted, and the benchmark quality volatility is selected from the task quality volatility dataset. The specific steps include: The weights of collaborative scenario categories are determined by the collaborative scenario categories. The scenario categories with the highest weights are selected as the core collaborative scenario categories, and the scenario feature data under these categories are extracted to form the core collaborative scenario category data. By comparing the actual values ​​of quality parameters of each work step with the corresponding quality standard values ​​of the core collaborative scenario category data, the deviation is calculated, and the maximum quality deviation value is obtained by judging the maximum value of the deviation as the work progresses from the standard value. Select the quality volatility that meets the preset conditions from the task quality volatility dataset as the benchmark quality volatility; The selected quality volatility rates are grouped according to the core collaborative scenario categories, and the quality volatility rate with the highest frequency in each group is taken as the benchmark quality volatility rate for that category.

[0034] The weights of each collaborative scenario category are determined based on their importance. Weight allocation must comprehensively consider the scenario's impact on overall surveying quality, operational complexity, and resource investment. Cross-regional, large-scale, multi-technology integrated scenarios, due to their wide scope, high collaboration difficulty, and decisive role in the quality of results, can have a weight set to 0.3. Single-regional, small-scale, single-technology application scenarios, with a smaller impact and simpler operations, can have a weight set to 0.1. Sorting by weight value from highest to lowest, the top-ranked scenario categories are selected as core collaborative scenario categories. If the top three weighted scenarios are cross-regional, large-scale, multi-technology integrated; cross-regional, medium-scale, dual-technology integrated; and single-regional, large-scale, multi-technology integrated, these three categories constitute the core collaborative scenario categories. Subsequently, scenario feature data under the core scenario categories are extracted, including the composition of participating entities within the scenario, details of technology application, regional geographical characteristics, and operational process specifications, and integrated to form the core collaborative scenario category data. For each core scenario of surveying and mapping tasks, the actual values ​​of the quality parameters of each operational stage are compared with the corresponding quality standard values, and the deviation between the two is calculated. For example, in a cross-regional, large-scale, multi-technology integrated scenario, the actual accuracy of the data acquisition stage is ±0.5 meters, the standard value is ±0.3 meters, and the deviation of this stage is 0.2 meters; the actual value of the data processing stage's result integrity is 92%, the standard value is 95%, and the deviation is 3%. The trajectory of the deviation of each stage is tracked as the operation progresses, and the fluctuation of the deviation in the entire operation process is analyzed. The maximum deviation from the standard value in the trajectory is found, and this value is the maximum quality deviation value of the task in the core collaborative scenario. If the maximum deviation of the above task is 0.2 meters in the data acquisition stage, 3% in the data processing stage, and 2% in the result review stage, the maximum quality deviation value is determined to be 3% of the data processing stage after comprehensive comparison. To select benchmark quality volatility from the generated task quality volatility dataset, first, clear preset screening criteria are set, usually based on industry quality standards and historical high-quality task data. For example, the quality volatility must be below 5% and remain stable for three consecutive tasks. Then, all quality volatility in the dataset is screened according to the preset screening criteria, and data that does not meet the criteria are removed. The quality volatility that meets the requirements is retained as candidate benchmark values. For example, if there are 100 quality volatility data in the dataset, 60 of them meet the criteria of being below 5% and stable, and these are the candidate benchmark values. The selected candidate benchmark quality volatility values ​​are grouped according to the core collaborative scenario category, with candidate values ​​belonging to the same core scenario category grouped together. For example, 20 candidate values ​​in the cross-regional, large-scale, multi-technology integration scenario are grouped together; 18 candidate values ​​in the cross-regional, medium-scale, dual-technology integration scenario are grouped together; and 22 candidate values ​​in the single-regional, large-scale, multi-technology integration scenario are grouped together. Frequency statistics are performed on the data in each group, counting the number of times each quality volatility value appears in the group, and the quality volatility value with the highest frequency is selected as the benchmark quality volatility value for that core collaborative scenario category. If, in the cross-regional, large-scale, multi-technology integration scenario group, the quality volatility value of 3% appears 8 times, which is the highest frequency in the group, then the benchmark quality volatility value corresponding to this scenario is 3%. The benchmark quality volatility values ​​of the other two groups are also determined in this way, and finally, the determination of the benchmark quality volatility values ​​for each core scenario is completed.

[0035] Based on core collaborative scenario category data and benchmark quality volatility, determine the initial correction time required to adjust the maximum quality deviation value to the acceptable quality range, specifically including the following steps: Extract key nodes for quality verification and standard processes for interaction between subjects from the core collaborative scenario category data, and determine the collaborative subjects involved in the correction work and the operation permissions of each subject; The deviation of the maximum quality deviation value from the benchmark state is calculated based on the benchmark quality volatility. The basic correction duration is determined by combining the deviation range with the standard response time of the main interaction in the core collaborative scenario. Based on the work process corresponding to the maximum quality deviation value, the number of associated entities in the core collaborative scenario of that process is called, and the basic correction time is adjusted in a stepwise manner according to the number of associated entities. When an additional associated entity is added, the corresponding proportion of coordination time is added to the basic correction time. Determine the verification levels that need to be passed during the correction process. When the verification level increases, the primary correction time is obtained by coordinating the correction time adjustment with the corresponding verification standard time of that level.

[0036] The key nodes for quality verification and the standard process for interaction between the main stakeholders are extracted from the core collaborative scenario category data. The key nodes for quality verification include data collection and review points, preliminary result verification points, and final quality approval points. The standard process for interaction between the main stakeholders clarifies the connection order and division of responsibilities of each participating entity in the correction work, and determines the collaborative entities involved in the correction work. For example, the correction work in the core scenario needs to involve the surveying and mapping implementation entity, the quality verification entity, and the technical support entity. At the same time, the operating permissions of each entity are clarified. The surveying and mapping implementation entity is responsible for the specific execution of deviation correction, the quality verification entity has the authority to review the correction results, and the technical support entity has the authority to provide correction technical guidance, so as to avoid the delay of correction work due to confusion of permissions. The baseline state is a stable quality state that conforms to the baseline quality volatility. The deviation magnitude directly reflects the severity of the quality deviation. For example, if the baseline quality volatility is 3% and the volatility corresponding to the maximum quality deviation value of the task is 9%, then the deviation magnitude is 9% - 3% = 6%. The basic correction time is determined by combining this deviation magnitude with the standard response time of the interaction between the main entities in the core collaborative scenario. The standard response time is the average response time of each entity under normal collaborative conditions. The larger the deviation magnitude and the longer the standard response time, the greater the basic correction time will be. Assuming that for every 2% increase in the deviation magnitude, the basic correction time increases by 1 hour and the standard response time is 2 hours, then the basic correction time for the above task is 2 + (6% ÷ 2%) × 1 = 5 hours. The more related entities there are, the higher the complexity of coordination and communication, requiring additional coordination time. A tiered adjustment rule should be set, for example, no additional coordination time is required when there are 1-2 related entities, 20% of the basic adjustment time is added when there are 3-5 related entities, and 40% of the coordination time is added when there are 6 or more related entities. If the maximum quality deviation value of the above task corresponds to the data processing stage, and there are 4 related entities in this stage with a basic adjustment time of 5 hours, then the adjusted time will be 5 + 5 × 20% = 6 hours. The process involves determining the required verification levels during the correction process. Verification levels are typically divided into Level 1, Level 2, and Level 3 verification. The higher the level, the more stringent the review process and the longer it takes. When a verification level is added, the primary correction time is calculated by adding the standard verification time corresponding to that level to the correction time adjusted after coordination. For example, if the standard verification time for Level 1 is 1 hour, Level 2 is 2 hours, and Level 3 is 3 hours, and the above task correction requires Level 2 verification with an adjusted time of 6 hours, then the primary correction time is 6 + 2 = 8 hours. By comprehensively considering factors such as the collaborating entities, the deviation range, the number of related factors, and the verification level, the primary correction time required to correct the maximum quality deviation value to the acceptable range can be accurately determined.

[0037] The collaborative scenario category data to be optimized is determined by classifying collaborative scenarios. The correlation quality impact coefficient set is used to determine the correlation quality impact data. Combining the collaborative scenario category data to be optimized and the correlation quality impact data, the secondary correction time required to correct the correlation quality impact data to the acceptable quality range is determined. The specific steps include: From the collaborative scenario categories, select scenario categories that are ranked after the core collaborative scenario categories in terms of weight as collaborative scenario categories to be optimized, extract the scenario operation characteristics under this category, and form collaborative scenario category data to be optimized; Based on the task quality impact transmission coefficient set, filter the sub-coefficient set corresponding to the collaborative scenario category to be optimized, determine the associated tasks affected by the maximum quality deviation value in the core collaborative scenario, and obtain the associated quality impact data by calculating the deviation between the actual value of the quality parameter of the associated task and the qualified range. Extract non-standard interaction processes of main entities from the collaborative scenario category data to be optimized, and determine the response patterns of main entities involved in correcting the data that affects the quality of related data; Based on the response pattern and the degree of deviation of the associated quality impact data, the simplified node for quality verification is obtained by judging the category of collaborative scenario data to be optimized. The quality verification simplifies the nodes and determines the verification steps that the correction process needs to go through. The secondary correction duration is obtained by calculating the preset verification time corresponding to each verification step.

[0038] In the collaborative scenario category system, scenarios following the core collaborative scenario category are selected as collaborative scenario categories to be optimized according to their weight ranking. These scenarios have a lower impact on the overall surveying and mapping quality than the core scenarios, and their quality stability can be improved through optimization. For example, the core scenario is a cross-regional, large-scale, multi-technology integrated scenario. The single-regional, medium-scale, dual-technology integrated scenarios, as well as the cross-regional, small-scale, single-technology application scenarios, can be listed as collaborative scenario categories to be optimized. Subsequently, the scenario operation characteristics under the scenarios to be optimized are extracted, including the main body collaboration efficiency, the maturity of technology application, and the standardization of work processes. For example, in the single-regional, medium-scale, dual-technology integrated scenario, the main body collaboration efficiency is moderate and the integration of remote sensing and GIS technologies is relatively mature. This information is integrated to form the collaborative scenario category data to be optimized. From the set of task quality impact transmission coefficients, sub-coefficient sets corresponding to the categories of collaborative scenarios to be optimized are selected. By using the quality impact transmission relationship between tasks in the sub-coefficient set, the associated tasks affected by the maximum quality deviation value in the core collaborative scenario are identified. For example, the maximum quality deviation value of a task in the core scenario affects the data processing task and the result delivery task in the scenario to be optimized through the transmission coefficient; these two tasks are the associated tasks. By calculating the deviation between the actual value of the quality parameter of the associated tasks and the acceptable range, the associated quality impact data is obtained. If the actual value of the result integrity of the data processing task is 88%, the acceptable range is 92%-98%, and the deviation is 4%; and the actual value of the timeliness of the result delivery task is a delay of 3 days, the acceptable range is on-time delivery to a delay of 1 day, and the deviation is 2 days, then this is the associated quality impact data. Extract non-standard processes of subject interaction from the data of collaborative scenarios to be optimized, such as untimely information transmission and unclear division of responsibilities between subjects; determine the subject response mode involved in correcting the associated quality impact data based on the non-standard processes, and classify the response modes into three categories: rapid response, normal response, and delayed response; if there are non-standard processes with information transmission delays between subjects in the scenario to be optimized, the normal response mode should be adopted when correcting to ensure orderly cooperation among subjects; if the impact of non-standard processes is small, the rapid response mode can be adopted to improve the efficiency of correction. Based on the determined response mode and the degree of deviation of the associated quality impact data, determine the simplified quality verification nodes corresponding to the collaborative scenario category data to be optimized. When the deviation is minor and the response mode is rapid response, the verification nodes can be simplified to reduce unnecessary review steps. When the deviation is severe and the response mode is conventional response, the core verification nodes must be retained to ensure the quality of the correction. For example, if the deviation of the associated quality impact data is minor and the response mode is rapid response, the original three-level verification can be simplified to two-level verification, and the data collection review point and the final quality review point can be determined as the simplified quality verification nodes. The quality verification simplifies the process by identifying the verification steps required for the correction process. Each verification step has a preset verification time, such as 1 hour for the data collection and audit point and 2 hours for the final quality assessment point. The preset times for all verification steps are added together to obtain the secondary correction time. If the correction requires two verification steps, the total time is 1 + 2 = 3 hours. This time is the secondary correction time required to correct the associated quality impact data to the acceptable range.

[0039] Based on the primary correction duration, secondary correction duration, and processing priority of collaborative scenarios, collaborative quality optimization processing is carried out on all surveying and mapping tasks and participating entities to obtain the collaborative operation processing results for land and resources surveying and mapping. Specifically, this includes the following steps: In collaborative scenarios, scenarios of the same priority are sorted from shortest to longest correction time to form an optimized processing sequence; The operation time period of each participating entity is allocated according to the primary correction time in the optimization processing sequence, and the correction operation content of the entity in the corresponding time period is clarified. Based on the duration of secondary corrections and the response patterns within the scenario, the correction rhythm of related tasks is statistically analyzed. Based on the comparison between the correction operation content and correction rhythm and the preset correction duration, the correction data of related scenarios are updated synchronously. When the quality parameters of all surveying and mapping tasks have entered the qualified range, the optimization processing records of each scenario are summarized to form the collaborative operation processing results of land and resources surveying and mapping.

[0040] Clearly define the processing priorities for collaborative scenarios. Core collaborative scenarios have higher priority than those requiring optimization. Scenarios within the same priority are then sorted by correction time from shortest to longest to form an optimization processing sequence. For example, cross-regional, large-scale, and multi-technology integration scenarios in core collaborative scenarios have the highest priority, with a primary correction time of 8 hours. Cross-regional, medium-scale, and dual-technology integration scenarios of the same priority have a primary correction time of 6 hours, and are sorted first. Single-regional, medium-scale, and dual-technology integration scenarios in those requiring optimization have lower priority, with a secondary correction time of 3 hours, and are ranked after core scenarios. This sorting ensures that resources are prioritized for critical and shorter correction tasks. Based on the primary correction time in the optimized processing sequence, dedicated operation time slots are allocated to each participating entity, while specifying the specific correction operations for each entity within the corresponding time slot. For example, in cross-regional, medium-scale, dual-technology integrated scenarios, the primary correction time is 6 hours. The first 2 hours are allocated to the surveying and mapping implementation entity, which is responsible for completing the deviation correction operations in the data processing stage. The middle 2 hours are allocated to the technical support entity, which is responsible for providing technical guidance and equipment debugging during the correction process. The last 2 hours are allocated to the quality verification entity, which completes the preliminary review of the correction results. Through precise matching of time slots and tasks, overlapping work or gaps in connection between entities are avoided, thereby improving correction efficiency. Based on the duration of secondary corrections and the main response mode within the scenario to be optimized, the correction rhythm of related tasks is statistically analyzed. The correction rhythm needs to be reasonably set in combination with the length of the secondary correction duration and the type of response mode. The rhythm is compact in the fast response mode and relatively slow in the regular response mode. For example, if the duration of the secondary correction for the scenario to be optimized is 3 hours, the response mode is fast response, and the related tasks include data verification and result adjustment, the correction of one related task is set to be completed every hour. That is, data verification is completed in the first hour, and result adjustment is completed in the second and third hours to ensure that the related tasks are carried out in an orderly manner and form an effective connection with the primary correction work. The correction operations of each entity and the correction pace of related tasks are compared in real time with the preset correction duration to dynamically monitor the progress of the correction work. If the surveying and mapping entity completes the data correction ahead of schedule within the allocated 2 hours, the correction data of related scenarios is updated in a timely manner, and the corrected quality parameters are fed back to the quality verification system. If the correction pace of related tasks is lagging behind, the work arrangements for subsequent periods are adjusted in a timely manner. The changes in the quality parameters of all surveying and mapping tasks are continuously tracked. When the quality parameters of all tasks have entered the qualified range, the optimization processing records of each scenario are summarized, including information such as the correction entity, operation content, time consumption, and quality improvement range. These are integrated to form a complete land and resources surveying and mapping collaborative operation processing result, providing a basis for subsequent operation quality assessment and process optimization.

[0041] A collaborative land and resources surveying and mapping system includes the following steps: Data extraction module: Construct a collaborative relationship network of the main participants in land and resources surveying and mapping operations, and extract collaborative interaction data of each main participant in the collaborative operation process and multi-dimensional quality parameters from the surveying and mapping results from the collaborative relationship network; Data generation module: Based on the collaborative interaction behavior data and quality parameters, calculate the quality volatility of each surveying and mapping task at different stages of collaborative operation, and generate a task quality volatility dataset; Identification module: Identifies the collaborative scenario categories involved in the surveying and mapping task, determines the quality impact transmission coefficient between surveying and mapping tasks under different collaborative scenario categories, and obtains the task quality impact transmission coefficient set; Extraction module: Determines core collaborative scenario category data by collaborative scenario category, extracts the maximum quality deviation value of each surveying and mapping task in the core collaborative scenario, and filters the benchmark quality volatility from the task quality volatility dataset; Judgment Module 1: Based on the core collaborative scenario category data and the benchmark quality volatility, determine the initial correction time required to correct the maximum quality deviation value to the acceptable quality range; Judgment Module 2: Determine the collaborative scenario category data to be optimized by the collaborative scenario category, use the task quality impact transmission coefficient set to judge the associated quality impact data, and combine the collaborative scenario category data to be optimized and the associated quality impact data to determine the secondary correction time required to correct the associated quality impact data to the qualified quality range. Output module: Based on the primary correction time, secondary correction time and the processing priority of collaborative scenarios, collaborative quality optimization processing is carried out on all surveying and mapping tasks and participating entities to obtain the processing results of land and resources surveying and mapping collaborative operations.

[0042] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for collaborative land and resources surveying and mapping, characterized in that, The method includes the following steps: Construct a collaborative relationship network among the main participants in land and resources surveying and mapping operations, and extract collaborative interaction data of each participant in the collaborative operation process and multi-dimensional quality parameters from the surveying and mapping results from the collaborative relationship network; Based on the collaborative interaction behavior data and quality parameters, the quality fluctuation rate of each surveying and mapping task at different stages of collaborative operation is calculated to generate a task quality fluctuation rate dataset. Identify the collaborative scenario categories involved in the surveying and mapping tasks, determine the quality impact transmission coefficients between surveying and mapping tasks under different collaborative scenario categories, and obtain the task quality impact transmission coefficient set; The core collaborative scenario category data is determined by the collaborative scenario category, the maximum quality deviation value of each surveying and mapping task in the core collaborative scenario is extracted, and the benchmark quality volatility is selected from the task quality volatility dataset. Based on the core collaborative scenario category data and the benchmark quality volatility, determine the initial correction time required to correct the maximum quality deviation value to the acceptable quality range; The collaborative scenario category data to be optimized is determined by the collaborative scenario category, the associated quality impact data is judged by the task quality impact transmission coefficient set, and the secondary correction time required to correct the associated quality impact data to the qualified quality range is determined by combining the collaborative scenario category data to be optimized and the associated quality impact data. Based on the primary correction time, secondary correction time, and processing priority of collaborative scenarios, collaborative quality optimization processing is carried out on all surveying and mapping tasks and participating entities to obtain the collaborative operation processing results of land and resources surveying and mapping.

2. The method for collaborative land and resources surveying and mapping processing according to claim 1, characterized in that, Constructing a collaborative network among the main participants in land and resources surveying and mapping operations includes the following steps: The types of participating entities are determined, including surveying and mapping implementation entities, data providers, quality verification entities, technical support entities, and results application entities, and identity identifiers are assigned to each participating entity. Collect business interaction information between various entities and categorize interactions based on the type of interaction content. A subject association matrix is ​​established based on identity. Each row in the matrix corresponds to the initiating subject, and each column corresponds to the receiving subject. The matrix element values ​​represent the interaction frequency between the initiating subject and the receiving subject. The association relationship level is obtained by classifying the interaction frequency. Construct a collaborative relationship network by combining subject type, interaction category, and association level.

3. The method for collaborative land and resources surveying and mapping processing according to claim 2, characterized in that, Extracting collaborative interaction data of each entity during collaborative operations and multi-dimensional quality parameters from surveying and mapping results from the collaborative relationship network includes the following steps: Based on the subject associations represented by nodes and connections in the collaborative relationship network, we capture the triggering conditions when each subject initiates an interaction, the interval between initiation and response, the integrity status of data transmission, task stage identifiers, and result attributes to obtain collaborative interaction behavior data. Based on the terrain type, regional characteristics, and application scenarios of the results involved in the collaborative operation, the basic quality dimension and the extended quality dimension are determined. Feature extraction rules are set for each quality dimension. By comparing the basic quality with the extended quality, the specific parameter values ​​under each dimension are obtained to obtain multi-dimensional quality parameters.

4. The method for collaborative land and resources surveying and mapping processing according to claim 3, characterized in that, Based on the collaborative interaction behavior data and quality parameters, the quality volatility of each surveying task at different stages of collaborative operation is calculated to generate a task quality volatility dataset, specifically including the following steps: The collaborative operation of the surveying and mapping task is divided into stages. For each stage, the response time deviation value and data transmission integrity fluctuation value are extracted from the collaborative interaction behavior data of the corresponding stage. Combined with the key indicators in the quality parameters of each stage, the response time deviation value, data transmission integrity fluctuation value and key indicators of each stage are assigned influence weights respectively. The quality fluctuation index of a given stage is obtained by calculating the values ​​of each parameter in the same stage and their corresponding influence weights. Calculate the difference in quality fluctuation index between two adjacent stages, and compare the difference in quality fluctuation index with the quality fluctuation index to obtain the quality fluctuation transmission rate between stages; By combining the quality fluctuation index of each stage with the quality fluctuation transfer rate between stages, a comprehensive quality fluctuation rate for each surveying and mapping task at different stages is generated. The overall quality volatility of all surveying and mapping tasks at each stage is summarized and categorized according to task identifier and stage order to form a task quality volatility dataset.

5. The method for collaborative land and resources surveying and mapping processing according to claim 4, characterized in that, Identify the collaborative scenario categories involved in the surveying and mapping tasks, determine the quality impact transmission coefficients between surveying and mapping tasks under different collaborative scenario categories, and obtain the task quality impact transmission coefficient set. This process includes the following steps: Based on the regional span of the surveying and mapping task, the number of participating entities, and the type of technology integration, collaborative scenario categories are divided. For each collaborative scenario category, the correlation features of all surveying and mapping tasks within the scenario are extracted, and a task correlation map within the scenario is constructed based on the correlation features. Extract the peak value of quality fluctuation in the preceding task and the response value of quality deviation in the subsequent task, and calculate the ratio of the response value of quality deviation to the peak value of quality fluctuation to obtain the basic influence coefficient; Based on the degree of correlation between tasks within the correlation graph, the basic influence coefficient is corrected, and the corrected basic influence coefficient is used as the quality influence transfer coefficient of the task pair. The quality impact transmission coefficients of all task pairs under the same collaborative scenario category are summarized, and then organized according to the scenario category and the direction of task association to form a sub-coefficient set for each scenario category. The sub-coefficient sets of all scenario categories are integrated to obtain the task quality impact transmission coefficient set.

6. The method for collaborative land and resources surveying and mapping processing according to claim 5, characterized in that, The core collaborative scenario category data is determined by classifying collaborative scenarios. The maximum quality deviation value of each surveying and mapping task in the core collaborative scenario is extracted, and the benchmark quality volatility is selected from the task quality volatility dataset. The specific steps include: The weights of collaborative scenario categories are determined by the collaborative scenario categories. The scenario categories with the highest weights are selected as the core collaborative scenario categories, and the scenario feature data under these categories are extracted to form the core collaborative scenario category data. By comparing the actual values ​​of quality parameters of each work step with the corresponding quality standard values ​​of the core collaborative scenario category data, the deviation is calculated, and the maximum quality deviation value is obtained by judging the maximum value of the deviation as the work progresses from the standard value. Select the quality volatility that meets the preset conditions from the task quality volatility dataset as the benchmark quality volatility; The selected quality volatility rates are grouped according to the core collaborative scenario categories, and the quality volatility rate with the highest frequency in each group is taken as the benchmark quality volatility rate for that category.

7. The method for collaborative land and resources surveying and mapping processing according to claim 6, characterized in that, Based on core collaborative scenario category data and benchmark quality volatility, determine the initial correction time required to adjust the maximum quality deviation value to the acceptable quality range, specifically including the following steps: Extract key nodes for quality verification and standard processes for interaction between subjects from the core collaborative scenario category data, and determine the collaborative subjects involved in the correction work and the operation permissions of each subject; The deviation of the maximum quality deviation value from the benchmark state is calculated based on the benchmark quality volatility. The basic correction duration is determined by combining the deviation range with the standard response time of the main interaction in the core collaborative scenario. Based on the work process corresponding to the maximum quality deviation value, the number of associated entities in the core collaborative scenario of that process is called, and the basic correction time is adjusted in a stepwise manner according to the number of associated entities. When an additional associated entity is added, the corresponding proportion of coordination time is added to the basic correction time. Determine the verification levels that need to be passed during the correction process. When the verification level increases, the primary correction time is obtained by coordinating the correction time adjustment with the corresponding verification standard time of that level.

8. The method for collaborative land and resources surveying and mapping processing according to claim 7, characterized in that, The collaborative scenario category data to be optimized is determined by classifying collaborative scenarios. The correlation quality impact coefficient set is used to determine the correlation quality impact data. Combining the collaborative scenario category data to be optimized and the correlation quality impact data, the secondary correction time required to correct the correlation quality impact data to the acceptable quality range is determined. The specific steps include: From the collaborative scenario categories, select scenario categories that are ranked after the core collaborative scenario categories in terms of weight as collaborative scenario categories to be optimized, extract the scenario operation characteristics under this category, and form collaborative scenario category data to be optimized; Based on the task quality impact transmission coefficient set, filter the sub-coefficient set corresponding to the collaborative scenario category to be optimized, determine the associated tasks affected by the maximum quality deviation value in the core collaborative scenario, and obtain the associated quality impact data by calculating the deviation between the actual value of the quality parameter of the associated task and the qualified range. Extract non-standard interaction processes of main entities from the collaborative scenario category data to be optimized, and determine the response patterns of main entities involved in correcting the data that affects the quality of related data; Based on the response pattern and the degree of deviation of the associated quality impact data, the simplified node for quality verification is obtained by judging the category of collaborative scenario data to be optimized. The quality verification simplifies the nodes and determines the verification steps that the correction process needs to go through. The secondary correction duration is obtained by calculating the preset verification time corresponding to each verification step.

9. A method for collaborative land and resources surveying and mapping processing according to claim 8, characterized in that, Based on the primary correction duration, secondary correction duration, and processing priority of collaborative scenarios, collaborative quality optimization processing is carried out on all surveying and mapping tasks and participating entities to obtain the collaborative operation processing results for land and resources surveying and mapping. Specifically, this includes the following steps: In collaborative scenarios, scenarios of the same priority are sorted from shortest to longest correction time to form an optimized processing sequence; The operation time period of each participating entity is allocated according to the primary correction time in the optimization processing sequence, and the correction operation content of the entity in the corresponding time period is clarified. Based on the duration of secondary corrections and the response patterns within the scenario, the correction rhythm of related tasks is statistically analyzed. Based on the comparison between the correction operation content and correction rhythm and the preset correction duration, the correction data of related scenarios are updated synchronously. When the quality parameters of all surveying and mapping tasks have entered the qualified range, the optimization processing records of each scenario are summarized to form the collaborative operation processing results of land and resources surveying and mapping.

10. A collaborative land and resources surveying and mapping processing system, applied to the collaborative land and resources surveying and mapping processing method according to any one of claims 1-9, characterized in that, Specifically, the following steps are included: Data extraction module: Construct a collaborative relationship network of the main participants in land and resources surveying and mapping operations, and extract collaborative interaction data of each main participant in the collaborative operation process and multi-dimensional quality parameters from the surveying and mapping results from the collaborative relationship network; Data generation module: Based on the collaborative interaction behavior data and quality parameters, calculate the quality volatility of each surveying and mapping task at different stages of collaborative operation, and generate a task quality volatility dataset; Identification module: Identifies the collaborative scenario categories involved in the surveying and mapping task, determines the quality impact transmission coefficient between surveying and mapping tasks under different collaborative scenario categories, and obtains the task quality impact transmission coefficient set; Extraction module: Determines core collaborative scenario category data by collaborative scenario category, extracts the maximum quality deviation value of each surveying and mapping task in the core collaborative scenario, and filters the benchmark quality volatility from the task quality volatility dataset; Judgment Module 1: Based on the core collaborative scenario category data and the benchmark quality volatility, determine the initial correction time required to correct the maximum quality deviation value to the acceptable quality range; Judgment Module 2: Determine the collaborative scenario category data to be optimized by the collaborative scenario category, use the task quality impact transmission coefficient set to judge the associated quality impact data, and combine the collaborative scenario category data to be optimized and the associated quality impact data to determine the secondary correction time required to correct the associated quality impact data to the qualified quality range. Output module: Based on the primary correction time, secondary correction time and the processing priority of collaborative scenarios, collaborative quality optimization processing is carried out on all surveying and mapping tasks and participating entities to obtain the processing results of land and resources surveying and mapping collaborative operations.

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