A tower crane jacking and lowering section operation monitoring data processing and storage method and system

Abstract

The present application relates to tower crane operation technical field, especially a kind of tower crane jacking and lowering section operation monitoring data processing storage method and system, comprising the following steps: by receiving and analyzing the data stream of each sensor source data of tower crane operation, including operation stage, load state and environmental condition, different operation stages and key action are applied to the special state change determination rule to filter, mark and selectively retain data, and operation stage, load and environmental change dynamically generate and adjust safety threshold to determine data processing mode, finally, the data after processing is organized and managed according to time sequence and is stored in batch processing mode, forms structured data set.The present application sets state change determination rule for different operation stages and key action of tower crane, only when state changes, screening and retaining monitoring data, realizing the differentiated processing of key data, reducing the occupation of invalid data repeated record to storage and processing resources.

Description

Technical Field

[0001] This invention relates to the field of tower crane operation technology, and in particular to a method and system for processing and storing monitoring data of tower crane top lifting section operation. Background Technology

[0002] Tower cranes are widely used in construction, and their lifting and lowering operations are critical aspects of their operation. These operations typically involve various factors such as changes in structural condition, load, and working environment. To ensure operational safety and traceability, existing technologies usually deploy multiple sensors during the lifting or lowering process to monitor relevant parameters such as the operation stage, load status, and environmental conditions in real time. The collected data is then used for operation monitoring, post-operation analysis, and record retention.

[0003] Traditional tower crane operation monitoring data is mostly collected and stored continuously using uniform rules. Due to the lack of differentiated judgment for operation stages and key actions, a large amount of data without state changes is repeatedly recorded, causing problems with storage and processing resources. Summary of the Invention

[0004] To overcome the above shortcomings, this invention provides a method and system for processing and storing monitoring data of tower crane top lifting section operation. It aims to improve the problem that traditional tower crane operation monitoring data is mostly collected and stored continuously using uniform rules, resulting in a large amount of data without state changes being repeatedly recorded, which occupies storage and processing resources.

[0005] In a first aspect, the present invention provides the following technical solution: a method for processing and storing monitoring data of tower crane top lifting section operation, comprising the following steps:

[0006] S1. Receive and parse the data from various sensor sources of tower crane operation to form a data stream that can be used for subsequent processing. The data stream includes real-time operation stage information, load status information and environmental condition information.

[0007] S2. For different operation stages and key action nodes of the tower crane, apply specialized state change judgment rules to various data streams. The rules define state change logic according to the operation characteristics and key actions of each operation stage. Filter out data that does not cross the safety range determined by the rules, and identify and selectively retain data that crosses the safety range of the rules.

[0008] S3. Based on the real-time operation stage, load status, and environmental conditions, dynamically determine the safety threshold for each data item to form a dynamic threshold; determine the processing method for each data item based on the formed dynamic threshold; the dynamic threshold is automatically adjusted as the operation stage progresses, the load changes, and the environmental conditions change;

[0009] S4. Organize and manage the data processed in S2 and S3 in chronological order, and store the data in the database according to batch processing logic to form a structured data set.

[0010] By adopting the above technical solution, specialized state change judgment rules are applied to the operation monitoring data for different operation stages and key action nodes of tower cranes. When state changes occur, the data is filtered and retained. Then, key state data can be differentiated and processed in the data processing stage. This improves the problem that traditional tower crane operation monitoring data is mostly collected and stored continuously using uniform rules. Due to the lack of differentiated judgment for operation stages and key actions, a large amount of data without state changes is repeatedly recorded, which occupies storage and processing resources.

[0011] Further, in step S1, the step of receiving and parsing data from various sensor sources of the tower crane operation to form a data stream that can be used for subsequent processing includes:

[0012] The raw data from each sensor source are received in the sampling order;

[0013] Extract the job stage information, load status information, and environmental condition information from the raw data;

[0014] The extracted data are integrated according to the collection time or operation stage to form a unified data flow.

[0015] Further, in step S2, the step of applying specialized state change determination rules to various types of data streams includes:

[0016] The data stream is divided into different analysis intervals according to the tower crane operation stage;

[0017] For each analysis interval, the data stream is compared according to a predefined state change logic;

[0018] Mark whether each piece of data triggers the specialization state change determination rule.

[0019] Further, in step S2, the step of filtering data that does not cross the safety interval determined by the rule includes:

[0020] Iterate through the marked data;

[0021] Data that does not trigger the specialized state change determination rule is excluded and removed from the subsequent processing queue.

[0022] Further, in step S2, the step of identifying and selectively retaining data that crosses the rule-based security interval includes:

[0023] Add a status identifier to the data that triggers the specialized state change determination rule;

[0024] The tagged data is saved to the subsequent processing queue for later dynamic threshold determination and batch storage.

[0025] Further, in step S3, the step of dynamically determining the security threshold for each data item and forming a dynamic threshold includes:

[0026] Obtain real-time job stage information, load status information, and environmental condition information for each piece of data;

[0027] The data is divided into corresponding threshold calculation intervals according to the work stage;

[0028] For each data item, apply a predefined threshold generation rule within its corresponding interval to determine the safety threshold of that data item;

[0029] Record the security threshold corresponding to each data point as a dynamic threshold.

[0030] Furthermore, in step S3, the step of determining the processing method for each data item based on the formed dynamic threshold includes:

[0031] Obtain the corresponding dynamic threshold for each data point;

[0032] Compare the data values ​​with dynamic thresholds;

[0033] Based on the comparison results, the data is classified into different subsequent processing queues;

[0034] A processing identifier is assigned to each data record.

[0035] Furthermore, in step S3, the step of automatically adjusting the dynamic threshold as the work phase progresses, the load changes, and the environmental conditions change includes:

[0036] Continuously acquire new information on job phases, load status, and environmental conditions;

[0037] Perform a matching check on the generated dynamic thresholds;

[0038] When the job phase, load status, or environmental conditions change, the dynamic threshold is updated according to predefined rules.

[0039] The updated dynamic threshold is then reapplied to the data determination.

[0040] Furthermore, in step S4, the step of storing the data into the database according to batch processing logic to form a structured data set includes:

[0041] The data processed in steps S2 and S3 are divided into multiple data batches according to time sequence or work stage;

[0042] Perform the data ingestion operation sequentially for each data batch;

[0043] During the inbound process, add a batch identifier and a corresponding time or operation stage identifier to each data entry;

[0044] All data batches are combined sequentially to form a structured data set.

[0045] Secondly, the present invention provides the following technical solution: a tower crane top lifting section operation monitoring data processing and storage system, the system comprising:

[0046] The data receiving and parsing module is used to receive and parse data from various sensor sources in tower crane operation, forming a data stream that can be used for subsequent processing. The data stream includes real-time operation stage information, load status information, and environmental condition information.

[0047] A dedicated judgment rule module is used to apply specialized state change judgment rules to various data streams for different operation stages and key action nodes of the tower crane. The rules define state change logic based on the operation characteristics and key actions of each operation stage, filter data that does not cross the safety range determined by the rules, and identify and selectively retain data that crosses the rule safety range.

[0048] The dynamic safety threshold module is used to dynamically determine the safety threshold of each data item based on the real-time operation stage, load status, and environmental conditions, forming a dynamic threshold; and to determine the processing method of each data item based on the formed dynamic threshold; the dynamic threshold is automatically adjusted as the operation stage progresses, the load changes, and the environmental conditions change.

[0049] The data organization and storage module is used to organize and manage the data processed by the dedicated judgment rule module and the dynamic security threshold module in chronological order, and to store the data in the database according to the batch processing logic to form a structured data set.

[0050] The present invention has the following beneficial effects:

[0051] 1. In this invention, specialized state change judgment rules are applied to the operation monitoring data for different operation stages and key action nodes of tower cranes. When state changes occur, the data is filtered and retained. Then, key state data is differentiated and processed in the data processing stage. This improves the problem that traditional tower crane operation monitoring data is mostly collected and stored continuously using uniform rules. Due to the lack of differentiated judgment for operation stages and key actions, a large amount of data without state changes is repeatedly recorded, which occupies storage and processing resources.

[0052] 2. In this invention, the safety threshold of each data item is dynamically determined based on the real-time operation stage, load status and environmental conditions, and the data is processed and judged based on the formed dynamic threshold. This enables the data processing logic to be adjusted with changes in the operation status, thereby improving the problem that the safety threshold in traditional tower crane operation monitoring is mostly set in a fixed manner, which cannot adapt to changes in different operation stages and load conditions, resulting in insufficient accuracy of data judgment.

[0053] 3. In this invention, the data after state change determination and dynamic threshold determination are organized in chronological order and managed by batch processing logic, thereby forming an ordered data set of operation monitoring data. This improves the problem that traditional tower crane monitoring data processing mostly adopts the real-time, line-by-line data entry method, which increases the complexity of data management due to the high coupling between data writing and processing timing. Attached Figure Description

[0054] Figure 1 This is a flowchart of a method for processing and storing monitoring data of tower crane top lifting section operation proposed in this invention;

[0055] Figure 2 This is an architecture diagram of a tower crane top lifting section operation monitoring data processing and storage system proposed in this invention. Detailed Implementation

[0056] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] Example 1: In the first embodiment of the present invention, the present invention provides a method for processing and storing monitoring data of tower crane top lifting section operation, such as... Figure 1 As shown, it includes the following steps:

[0058] S1. Receive and parse the data from various sensor sources of tower crane operation to form a data stream that can be used for subsequent processing. The data stream includes real-time operation stage information, load status information and environmental condition information.

[0059] Furthermore, in step S1, the steps of receiving and parsing data from various sensor sources of the tower crane operation to form a data stream that can be used for subsequent processing include:

[0060] The raw data from each sensor source is received in the order of sampling.

[0061] Extract job phase information, load status information, and environmental condition information from the raw data;

[0062] The extracted data are integrated according to the collection time or operation stage to form a unified data flow.

[0063] Specifically, by sequentially receiving, parsing, and integrating data from various sensor sources during tower crane lifting or lowering operations, a data stream is formed, based on a consistent processing flow of time sequence and operational semantics. During the operation, each sensor source continuously outputs raw monitoring data according to a predetermined sampling frequency. The raw monitoring data includes at least stage indicator data characterizing the current operation stage, load-related data reflecting the tower crane's stress state, and environmental-related data reflecting the external conditions of the operation. The data is arranged according to the sampling time as the basic sequence identifier upon reception. After data reception is completed, the raw monitoring data is parsed, mapping the data items output by different sensor sources to a unified data field, and extracting data related to the operation stage. The extraction process is based on the correspondence between the sensor source data type and the field meaning. The extracted data is then integrated according to the collection time sequence or the operation stage sequence, so that the stage information, load status information and environmental condition information at the same time point or the same operation stage form corresponding data records, thereby constructing a data stream that can be used for subsequent processing. The formed data stream serves as a unified data input carrier and is used in subsequent steps to execute the operation stage-related state change judgment rules and determine and process dynamic safety thresholds, providing basic data support for subsequent data filtering, identification and batch storage.

[0064] S2. For different operating stages and key action nodes of tower cranes, apply specialized state change judgment rules to various data streams. The rules define state change logic based on the operating characteristics and key actions of each operating stage, filter data that does not cross the safe zone determined by the rules, and identify and selectively retain data that crosses the safe zone of the rules.

[0065] Furthermore, in step S2, the steps of applying specialized state change determination rules to various data streams include:

[0066] The data stream is divided into different analysis intervals according to the tower crane operation stage;

[0067] For each analysis interval, the data stream is compared according to a predefined state change logic;

[0068] Mark each data item to determine whether it triggers the specialization state change judgment rule.

[0069] Specifically, by introducing a state change judgment process corresponding to the tower crane operation stage, targeted filtering and identification of the data stream formed in step S1 is achieved, based on rule matching processing according to the operation stage division. The data stream formed in the previous stage is used as input, and each data item in the data stream is associated with at least real-time operation stage information, load status information, and environmental condition information. The operation stage information indicates the lifting or lowering operation stage corresponding to the current data. First, based on the operation stage information carried in the data, the continuous data stream is divided into several analysis intervals. Each analysis interval corresponds to a specific operation stage or key action node, thus ensuring that subsequent judgments are only made within the same operation semantic range. After completing the interval division, the data in each analysis interval is compared one by one according to the predefined state change logic of that operation stage. The state change logic describes the allowed changes and boundaries of key parameters under that stage. When the state change reflected in the data satisfies the predefined logic, the data is determined to trigger a specialized state change judgment rule. The process can be represented as a comparison relationship based on a state function; the state determination result can be represented as: R=f(D,G); where D represents a data point within the current analysis interval, G represents the set of state change logics corresponding to the operation stage, and the function f is used to output whether the data meets the state change determination rules; for cases where the determination result is "not triggered", the corresponding data is considered not to have crossed the safe zone determined by the rules and is filtered in subsequent processing; for cases where the determination result is "triggered", the corresponding data is marked with a state identifier and selectively retained; the resulting identifier is used as part of the output data and is directly used in subsequent steps to determine the dynamic safety threshold and the data processing method, so that subsequent processing is only performed on data related to the operation state change; the safe zone is limited by the predefined state change logic under the corresponding operation stage, which describes the allowable range of change of key parameters within the operation stage. When the parameter change reflected by the data does not exceed the range of change, the corresponding data is considered to be within the safe zone.

[0070] Furthermore, in step S2, the step of filtering data that does not cross the safe zone defined by the rules includes:

[0071] Iterate through the marked data;

[0072] Data that does not trigger the specialization state change determination rule is excluded and removed from the subsequent processing queue.

[0073] Specifically, data filtering for data that does not cross the safe zone defined by the rules is a further screening process of the data stream after the specialized state change determination is completed. This is achieved through sequential traversal and conditional exclusion based on the determination results. The input data is a set of labeled data formed after the previous stage of state change determination. Each data item is associated with a state determination identifier, which is generated by the aforementioned state change determination rules and indicates whether the corresponding data triggers the rule. First, the labeled data is traversed in the order of generation. During the traversal, the state determination identifier corresponding to each data item is read, and the determination of whether the data triggers the specialized state change determination rule is based on this identifier. This determination process can be abstractly represented as: F = ϕ(R); In this context, R represents the status determination result of a single data entry, and the function ϕ is used to output the filtering determination result of whether the data needs to be retained. When the determination result shows that the data has not triggered the specialized status change determination rule, the data is removed from the subsequent processing queue and will no longer participate in the subsequent dynamic threshold determination and batch data entry processing. When the determination result shows that the data has triggered the specialized status change determination rule, the data remains unchanged in the subsequent processing queue. Through the above traversal and exclusion steps, the output result is a set of data that only triggers the specialized status change determination rule. This result is used as the input basis for determining the dynamic safety threshold and the data processing method in the subsequent steps, so that the subsequent processing flow only focuses on data related to changes in the job status.

[0074] Furthermore, in step S2, the steps of identifying and selectively retaining data that crosses the rule-defined security zone include:

[0075] Add a status identifier to the data that triggers the specialization status change determination rule;

[0076] The tagged data is saved to the subsequent processing queue for later dynamic threshold determination and batch storage.

[0077] Specifically, the identification and selective retention of data that crosses the safety zone defined by the rules is a process of marking and queue management of data that meets the retention conditions after completing the state change judgment and filtering. This is achieved through state assignment and sequential storage based on the judgment results. After processing the data set using specialized state change judgment rules, each data item is associated with a corresponding judgment result, obtained by comparing each data item in the data stream with the state change logic corresponding to the work stage. When the judgment result of a data item indicates that it crosses the safety zone defined by the rules, a state identifier is added to that data. This state identifier indicates that the data has triggered the specialized state change judgment rule, and can be expressed as: S = h(R); where R represents the state change judgment rule of the data. The result is determined by function h, which generates the corresponding status identifier S. Subsequently, the data with added status identifiers is saved to the subsequent processing queue according to its chronological order in the data stream. The subsequent processing queue serves as the input set for the next stage of processing, used for subsequent dynamic security threshold determination, threshold-based data processing method judgment, and batch data entry processing. Through the above steps, the output is a data set containing status identifiers. This result is used in subsequent steps to distinguish and manage different data processing paths, ensuring that only data with status changes continues to undergo higher-level data processing. The subsequent processing queue is used to temporarily store data that has completed status change judgment and has been selectively retained, serving as the input set for subsequent dynamic threshold determination and batch data entry processing.

[0078] S3. Based on the real-time operation stage, load status, and environmental conditions, dynamically determine the safety threshold for each data item to form a dynamic threshold; determine the processing method for each data item based on the formed dynamic threshold; the dynamic threshold is automatically adjusted as the operation stage progresses, the load changes, and the environmental conditions change;

[0079] Furthermore, in step S3, the steps of dynamically determining the security threshold for each data item and forming the dynamic threshold include:

[0080] Obtain real-time job stage information, load status information, and environmental condition information for each piece of data;

[0081] The data is divided into corresponding threshold calculation intervals according to the work stage;

[0082] For each data item, apply a predefined threshold generation rule within its corresponding interval to determine the safety threshold of that data item;

[0083] Record the security threshold corresponding to each data point as a dynamic threshold.

[0084] Specifically, the process of dynamically determining the safety threshold for each data item is achieved by jointly driving threshold generation rules through multi-dimensional operating conditions, based on the completed status identification and data queue division. This is implemented using a dynamic threshold determination method based on work stage interval mapping and rule-based calculation. The input data includes real-time work stage information, load status information, and environmental condition information associated with each data item. The work stage information comes from the work process judgment results, the load status information comes from the real-time acquisition results of the corresponding sensor data, and the environmental condition information comes from environmental monitoring data related to the work site. First, the data is mapped to a preset threshold calculation interval according to the work stage information, with different intervals corresponding to different sets of threshold generation rules. Then, within each interval, a predefined threshold generation rule is applied to each data item. Then, taking into account the impact of load status and environmental conditions on the safety boundary, the safety threshold corresponding to the data item is determined. This process can be expressed as: T=g(P,L,E); where P represents the real-time operation stage, L represents the load status, E represents the environmental conditions, the function g represents the threshold generation rule for the corresponding operation stage interval, and the output result T is the safety threshold of the data item. The determined safety threshold is associated with the corresponding data item and recorded to form a dynamic threshold set. The dynamic threshold is automatically updated as the operation stage progresses, the load changes, and the environmental conditions change. This dynamic threshold serves as the basis for determining the subsequent data processing method, used to determine whether the data item needs to continue to perform high-frequency monitoring, long-term retention, or enter the batch storage processing flow, thereby realizing the dynamic adjustment and hierarchical management of the data processing path.

[0085] Furthermore, in step S3, the step of determining the processing method for each data item based on the formed dynamic threshold includes:

[0086] Obtain the corresponding dynamic threshold for each data point;

[0087] Compare the data values ​​with dynamic thresholds;

[0088] Based on the comparison results, the data is classified into different subsequent processing queues;

[0089] A processing identifier is assigned to each data record.

[0090] Specifically, the process of determining the processing method for each data item based on the formed dynamic threshold is achieved through threshold comparison and regular queue allocation, on the basis of the completed dynamic threshold determination. This includes obtaining the corresponding dynamic threshold for each data item entering the judgment stage and performing numerical comparison; the input data includes the real-time value of the data item and the dynamic threshold associated with that data item; where the real-time value of the data item comes from the valid data collected by the sensor and processed by the previous steps, and the dynamic threshold comes from the threshold results generated and recorded based on the operation stage, load status, and environmental conditions; the judgment logic of comparing the data value with the dynamic threshold can be expressed as: R=h(X, (T); where X represents the real-time value of the data item, T represents the dynamic threshold corresponding to the data item, function h represents the preset processing method judgment rule, and the output result R represents the processing category corresponding to the data item; based on the comparison results, the data is divided into different subsequent processing queues, and different subsequent processing queues correspond to subsequent real-time monitoring, key analysis or batch storage processing processes, and a processing identifier is assigned to each data record and the corresponding subsequent processing queue; the processing identifier serves as the basis for subsequent data scheduling and storage strategy selection, and is used to determine whether the data enters the immediate processing path or the delayed batch storage path in subsequent steps, thereby realizing the dynamic allocation and storage management of data processing load.

[0091] Furthermore, in step S3, the step of automatically adjusting the dynamic threshold as the work phase progresses, the load changes, and the environmental conditions change includes:

[0092] Continuously acquire new information on job phases, load status, and environmental conditions;

[0093] Perform a matching check on the generated dynamic thresholds;

[0094] When the job phase, load status, or environmental conditions change, the dynamic threshold is updated according to predefined rules.

[0095] The updated dynamic threshold is then reapplied to the data determination.

[0096] Specifically, the dynamic threshold automatically adjusts according to changes in the operation stage, load status, and environmental conditions. This is achieved through state awareness and rule-driven updates, building upon an established dynamic threshold generation and judgment mechanism. The process involves continuously acquiring the latest operation stage, load status, and environmental condition information associated with the data items. This information comes from the real-time output of the tower crane control system, load monitoring device, and environmental monitoring device, and is matched against the generated dynamic thresholds to determine if the current threshold is still applicable to the latest operating state. When a change in the operation stage, load status, or environmental conditions is detected, the threshold update process is triggered, adjusting the original threshold according to predefined update rules. A dynamic threshold is used for correction, and its update logic can be expressed as: T′=g(S,L,E); where S represents the current operation stage information, L represents the current load status information, E represents the current environmental condition information, the function g represents the pre-set threshold update rule, and the output result T′ represents the updated dynamic threshold. The updated dynamic threshold is recorded and reused in the subsequent data judgment process to replace the original threshold in the comparison of data values ​​with the threshold, thereby ensuring that the judgment of the data processing method is always consistent with the actual operating status of the tower crane. This result is used to support subsequent data classification, subsequent processing queue allocation, and storage strategy selection to achieve continuous adaptation and effective management of monitoring data.

[0097] S4. Organize and manage the data processed in S2 and S3 in chronological order, and store the data in the database according to batch processing logic to form a structured data set.

[0098] Furthermore, in step S4, the step of storing the data into the database according to the batch processing logic to form a structured data set includes:

[0099] The data processed in steps S2 and S3 are divided into multiple data batches according to time sequence or work stage;

[0100] Perform the data ingestion operation sequentially for each data batch;

[0101] During the inbound process, add a batch identifier and a corresponding time or operation stage identifier to each data entry;

[0102] All data batches are combined sequentially to form a structured data set.

[0103] Specifically, the data processed in the aforementioned steps is entered into the database according to batch processing logic. This is achieved by combining temporary data storage with centralized data entry at night, based on the completion of state change judgment and dynamic threshold judgment. During the data generation stage, the data processed in steps S2 and S3 is continuously written to the subsequent processing queue. The subsequent processing queue is used to temporarily store the data that has been judged. The data includes the corresponding data value, work stage information, processing identifier, and time information, where the time information comes from the data acquisition time or the work stage switching time. During the daytime work monitoring period, only the data is judged and temporarily stored, and the data entry operation is not performed immediately to ensure the continuity of real-time monitoring and judgment processing. During the preset nighttime period, the batch data entry process is triggered, and the data in the subsequent processing queue is grouped according to time order or work stage consistency to form multiple data batches. Each data batch can be represented as: B k ={d1,d2,…,d n}; where B k d represents the k-th data batch. i This indicates data items belonging to the same time interval or the same work stage. During the nighttime batch warehousing process, each data batch is sequentially warehousing, and a corresponding batch identifier and time identifier or work stage identifier are written to each data item during the warehousing process, thereby establishing the association between the data item and its batch. All data batches that have completed nighttime warehousing are combined in batch order to form a structured data set with a clear time dimension or work stage dimension. This structured data set is used to support subsequent historical data backtracking, statistical analysis, and long-term storage management. At the same time, by limiting the centralized warehousing operation to the nighttime period, the resource consumption impact on daytime real-time monitoring and judgment processing is avoided.

[0104] Example 2: During tower crane lifting or lowering operations at construction sites, the load-bearing state, motion characteristics, and safety risks of the tower crane vary significantly at different operational stages. Various sensors continuously generate high-frequency monitoring data during operation. If this data is stored indiscriminately in real-time, it can easily lead to severe data redundancy and excessive system write load, thereby affecting real-time monitoring and safety assessment during operations. To solve the above problems, a tower crane lifting / lowering operation monitoring data processing and storage system provided by this invention is adopted, the structure of which is as follows: Figure 2 As shown. The specific implementation process of this system in actual operation is as follows:

[0105] First, the data receiving and parsing module receives and parses the data from various sensor sources in the tower crane operation, forming a data stream in a unified format, and extracts real-time operation stage information, load status information, and environmental condition information, thereby providing basic data support for subsequent data processing based on operation stage and operating status.

[0106] Subsequently, the dedicated judgment rule module applies corresponding state change judgment rules to various data streams based on the real-time operation stage information and the different operation stages and key action nodes of the tower crane. Data that has not crossed the safety zone is filtered out, while data that has crossed the safety zone is identified and selectively retained, thereby reducing invalid data from entering the subsequent processing flow.

[0107] Based on this, the dynamic safety threshold module dynamically determines the safety threshold of each data item according to the real-time operation stage, load status and environmental conditions, and determines the data processing method according to the formed dynamic threshold. The dynamic threshold is automatically adjusted as the operation stage progresses, the load changes and the environmental conditions change, so as to adapt to the safety judgment requirements under different operation stages.

[0108] Finally, the data organization and storage module organizes and manages the data processed by the dedicated judgment rule module and the dynamic safety threshold module in chronological order, and executes the storage operation according to the batch processing logic. The large-scale data storage process is scheduled to be completed at night or during low-load periods, thereby ensuring real-time monitoring capabilities during operations while realizing structured storage of monitoring data.

[0109] Through the above implementation methods, the present invention can achieve the screening, dynamic judgment and batch storage of monitoring data in tower crane lifting or lowering operations, taking into account both operational safety monitoring and system operating efficiency.

[0110] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for processing and storing monitoring data of tower crane top lifting section operation, characterized in that, Includes the following steps: S1. Receive and parse the data from various sensor sources of tower crane operation to form a data stream that can be used for subsequent processing. The data stream includes real-time operation stage information, load status information and environmental condition information. S2. For different operation stages and key action nodes of the tower crane, apply specialized state change judgment rules to various data streams. The rules define state change logic according to the operation characteristics and key actions of each operation stage. Filter out data that does not cross the safety range determined by the rules, and identify and selectively retain data that crosses the safety range of the rules. S3. Based on the real-time operation stage, load status, and environmental conditions, dynamically determine the safety threshold for each data item to form a dynamic threshold; determine the processing method for each data item based on the formed dynamic threshold; the dynamic threshold is automatically adjusted as the operation stage progresses, the load changes, and the environmental conditions change; S4. Organize and manage the data processed in S2 and S3 in chronological order, and store the data in the database according to batch processing logic to form a structured data set.

2. The method for processing and storing monitoring data of tower crane top lifting section operation according to claim 1, characterized in that, In step S1, the step of receiving and parsing data from various sensor sources of the tower crane operation to form a data stream that can be used for subsequent processing includes: The raw data from each sensor source are received in the sampling order; Extract the job stage information, load status information, and environmental condition information from the raw data; The extracted data are integrated according to the collection time or operation stage to form a unified data flow.

3. The method for processing and storing monitoring data of tower crane top lifting section operation according to claim 1, characterized in that, In step S2, the step of applying specialized state change determination rules to various types of data streams includes: The data stream is divided into different analysis intervals according to the tower crane operation stage; For each analysis interval, the data stream is compared according to a predefined state change logic; Mark whether each piece of data triggers the specialization state change determination rule.

4. The method for processing and storing monitoring data of tower crane top lifting section operation according to claim 1, characterized in that, In step S2, the step of filtering data that does not cross the safety interval determined by the rule includes: Iterate through the marked data; Data that does not trigger the specialized state change determination rule is excluded and removed from the subsequent processing queue.

5. The method for processing and storing monitoring data of tower crane top lifting section operation according to claim 1, characterized in that, In step S2, the step of identifying and selectively retaining data that crosses the rule-based security interval includes: Add a status identifier to the data that triggers the specialized state change determination rule; The tagged data is saved to the subsequent processing queue for later dynamic threshold determination and batch storage.

6. The method for processing and storing monitoring data of tower crane top lifting section operation according to claim 1, characterized in that, In step S3, the step of dynamically determining the security threshold for each data item and forming the dynamic threshold includes: Obtain real-time job stage information, load status information, and environmental condition information for each piece of data; The data is divided into corresponding threshold calculation intervals according to the work stage; For each data item, apply a predefined threshold generation rule within its corresponding interval to determine the safety threshold of that data item; Record the security threshold corresponding to each data point as a dynamic threshold.

7. The method for processing and storing monitoring data of tower crane top lifting section operation according to claim 1, characterized in that, In step S3, the step of determining the processing method for each data item based on the formed dynamic threshold includes: Obtain the corresponding dynamic threshold for each data point; Compare the data values ​​with dynamic thresholds; Based on the comparison results, the data is classified into different subsequent processing queues; Each data record is assigned a processing identifier.

8. A method for processing and storing monitoring data of tower crane top lifting section operation according to claim 1, characterized in that, In step S3, the step of automatically adjusting the dynamic threshold as the work phase progresses, the load changes, and the environmental conditions change includes: Continuously acquire new information on job phases, load status, and environmental conditions; Perform a matching check on the generated dynamic thresholds; When the job phase, load status, or environmental conditions change, the dynamic threshold is updated according to predefined rules. The updated dynamic threshold is then reapplied to the data determination.

9. A method for processing and storing monitoring data of tower crane top lifting section operation according to claim 1, characterized in that, In step S4, the step of storing the data into the database according to batch processing logic to form a structured data set includes: The data processed in steps S2 and S3 are divided into multiple data batches according to time sequence or work stage; Perform the data ingestion operation sequentially for each data batch; During the inbound process, add a batch identifier and a corresponding time or operation stage identifier to each data entry; All data batches are combined sequentially to form a structured data set.

10. A tower crane top lifting section operation monitoring data processing and storage system, characterized in that, A method for processing and storing monitoring data of tower crane top lifting section operation as described in any one of claims 1-9, the system comprising: The data receiving and parsing module is used to receive and parse data from various sensor sources in tower crane operation, forming a data stream that can be used for subsequent processing. The data stream includes real-time operation stage information, load status information, and environmental condition information. A dedicated judgment rule module is used to apply specialized state change judgment rules to various data streams for different operation stages and key action nodes of the tower crane. The rules define state change logic based on the operation characteristics and key actions of each operation stage, filter data that does not cross the safety range determined by the rules, and identify and selectively retain data that crosses the rule safety range. The dynamic safety threshold module is used to dynamically determine the safety threshold of each data item based on the real-time operation stage, load status, and environmental conditions, forming a dynamic threshold; and to determine the processing method of each data item based on the formed dynamic threshold; the dynamic threshold is automatically adjusted as the operation stage progresses, the load changes, and the environmental conditions change. The data organization and storage module is used to organize and manage the data processed by the dedicated judgment rule module and the dynamic security threshold module in chronological order, and to store the data in the database according to the batch processing logic to form a structured data set.

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