A method for weighing data acquisition and processing in silo weighbridge sales models

Abstract

This invention discloses a method for weighing data acquisition and processing in a silo weighbridge sales model, belonging to the field of weighing data acquisition and processing technology. Specifically, it includes the following steps: real-time acquisition of the processing load information of each weighing data point, followed by analysis to generate a data pressure coefficient and buffer occupancy index for each weighing data point; construction of a load assessment model based on the generated data pressure coefficient and buffer occupancy index to generate a load assessment coefficient for each weighing data point, followed by analysis to assess the processing priority of each weighing data point, and classification of each weighing data point into three categories based on the assessment results: high-priority weighing data, medium-priority weighing data, and low-priority weighing data. This invention solves the problems of real-time processing and buffer management of weighing data under high load conditions, achieving data transmission integrity and efficient allocation of system resources.

Description

Technical Field

[0001] This invention relates to the field of weighing data acquisition and processing technology, specifically to a method for weighing data acquisition and processing in the silo weighbridge sales model. Background Technology

[0002] In modern coal mine production and logistics systems, efficient and accurate loading and weighing processes are crucial for ensuring fairness in the trading of bulk commodities such as coal, improving transportation efficiency, and optimizing inventory management. The silo weighbridge sales model refers to the real-time weighing of goods loaded onto trucks using weighbridges located within silos during the trading of bulk commodities such as coal, enabling immediate confirmation of quantity by both parties. This model not only enhances transaction transparency but also promotes efficient logistics operations. Therefore, systematically collecting and processing weighing data from the silo weighbridge sales model ensures timely updates and accurate recording of weighing information, providing reliable data support for all parties involved in the transaction, thereby further optimizing the overall logistics management and decision-making process.

[0003] Existing weighing data acquisition and processing technologies for silo weighbridge sales typically involve multiple stages. First, the weighbridge system monitors the weight of loading vehicles in real time using sensors. When a vehicle enters the weighing area, the sensors automatically record its weight information and transmit this information to the central processing system in real time via data acquisition equipment. During data transmission, the system performs preliminary data verification to ensure accuracy. Next, the data processing software analyzes and stores the collected weighing data, while also correlating it with other relevant information (such as loading time, vehicle number, and cargo type) to form a complete weighing record. Finally, this data is integrated into the logistics management system, allowing both parties to the transaction to query and reconcile accounts in real time, supporting subsequent transaction decisions and inventory management.

[0004] The existing technology has the following shortcomings:

[0005] During peak hours when multiple vehicles are being loaded continuously, different vehicles with varying loads need to frequently enter the weighing area and have their weight data recorded in real time. Under this high-load condition, the data processing and transmission demands of the weighbridge system increase dramatically. The high-frequency switching of processing and transmission tasks leads to a significant increase in system load, easily resulting in delays in weighing data recording or incomplete data storage. Because frequent loading operations require the weighing system to process large amounts of data continuously in a short period, the processing speed and data buffer capacity of current technology are insufficient. This prevents the system from ensuring that the weighing data of each vehicle is recorded and uploaded to the central server in a timely manner, causing missing real-time data and decreased accuracy. Consequently, data delays or incomplete recordings directly lead to inaccurate recording of the number of vehicles loaded, affecting the transparency and fairness of transactions. Inconsistencies may be discovered during reconciliation between the transacting parties, increasing reconciliation costs, prolonging reconciliation time, and potentially triggering transaction disputes and damaging the trust relationship between the parties.

[0006] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] The purpose of this invention is to provide a method for collecting and processing weighing data for silo weighbridge sales, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for collecting and processing weighing data for silo weighbridge sales, specifically including the following steps:

[0009] During peak periods when multiple vehicles are continuously loaded, when the vehicles enter the silo weighing area and are weighed, the load status information of all weighing data generated when weighing each vehicle is collected in real time, and a real-time analysis framework for the data load status is established based on the load status information of all the collected weighing data.

[0010] Real-time acquisition of processing load information for each weighing data point, followed by analysis to generate data pressure coefficient and cache occupancy index for each weighing data point;

[0011] A load assessment model is constructed based on the data pressure coefficient and buffer usage index of each generated weighing data, and the load assessment coefficient of each weighing data is generated. After generation, the model is analyzed to assess the processing priority of each weighing data and classify each weighing data into three categories: high-priority weighing data, medium-priority weighing data, and low-priority weighing data based on the assessment results.

[0012] Based on the division results of each weighing data, corresponding upload and caching strategies are implemented for high-priority weighing data, medium-priority weighing data and low-priority weighing data respectively;

[0013] The system continuously monitors the transmission rate and buffer status information, and dynamically adjusts the parameter settings of the load assessment model based on real-time analysis results.

[0014] Preferably, the processing load information of each weighing data point is acquired in real time and analyzed after acquisition to generate the data pressure coefficient and cache occupancy index for each weighing data point. Specifically, this includes the following steps:

[0015] The processing load information of each weighing data point is acquired in real time and preprocessed after acquisition.

[0016] Extract data pressure information and cache status information from the processing load information of each preprocessed weighing data;

[0017] The data pressure information and cache status information in the processing load information of each extracted weighing data are analyzed to generate the data pressure coefficient and cache occupancy index for each weighing data.

[0018] Preferably, the logic for obtaining the data pressure coefficient of each weighing data point is as follows:

[0019] Extract the data pressure information from the processing load information of each preprocessed weighing data point. Specifically, this includes the amount of data received by the weighing system at different times within a given period, the time difference between processing each weighing data point, and the rate at which the weighing system transmits each weighing data point to the central server. These are then calibrated as follows: and This represents the amount of data received by the weighing system at time m within a certain time period, specifically the amount of data from the i-th weighing data point. This represents the time difference in which the weighing system processes the i-th weighing data at time m within a certain time period. This represents the rate at which the weighing system transmits the i-th weighing data to the central server at time m within a certain time period, where i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., g, and k and g are both positive integers;

[0020] The pressure coefficient for each weighing data point is calculated using the following formula:

[0021]

[0022] In the formula, DPC i Let be the data pressure coefficient of the i-th weighing data.

[0023] Preferably, the logic for obtaining the cache usage index of each weighing data is as follows:

[0024] Extract the cache status information from the processing load information of each preprocessed weighing data point. Specifically, this includes the number of times the weighing system processed each weighing data point at different times within a historical period, as well as the remaining cache space and the percentage of space used at different times within the period, and label them accordingly. CR m and UC m , CR represents the number of times the weighing system processes the i-th weighing data at time n within a historical period. m This represents the remaining space in the weighing system cache at time m within a certain time period, UC m This represents the percentage of the weighing system buffer used at time m within a certain time period, where i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., g, n = 1, 2, 3, ..., h, and k, g, and h are all positive integers.

[0025] The buffer usage index for each weighing data point is calculated using the following formula:

[0026]

[0027] In the formula, COI i Let be the cache usage index for the i-th weighing data.

[0028] Preferably, a load assessment model is constructed based on the data pressure coefficient and buffer usage index of each generated weighing data point to generate the load assessment coefficient for each weighing data point. This specifically includes the following steps:

[0029] Collect the data pressure coefficient, buffer usage index, and corresponding load assessment coefficient of several weighing data points generated over a period of time, and label them as follows: and x represents the number of the data pressure coefficient, buffer usage index and corresponding load assessment coefficient of several weighing data generated in the past period, x = 3, 4, 5, ..., d, where d is a positive integer, and the collected data in the past period are formed into a historical dataset;

[0030] A multiple regression model was chosen as the load assessment model, and it was trained using historical datasets to determine the values ​​of the regression coefficients, based on the formula:

[0031]

[0032] In the formula, β0, β1, and β2 are regression coefficients;

[0033] By minimizing the error between the predicted and actual values, the regression coefficients are optimized, and the values ​​of the regression coefficients β0, β1, and β2 are finally determined.

[0034] Using the finalized regression coefficients, the data pressure coefficients (DPC) of each real-time generated weighing data point are input into the constructed load assessment model. i and Cache Usage Index (COI) i The load assessment coefficient (LEC) for each weighing data point is generated in real time. i .

[0035] Preferably, the load assessment coefficient (LEC) of each generated weighing data is used. i Compared with the pre-set load assessment coefficient threshold range [LEC] min LEC max The data is compared, and the priority of each weighing data point is assessed based on the comparison results. Based on the assessment results, each weighing data point is divided into three categories: high-priority weighing data, medium-priority weighing data, and low-priority weighing data. The specific comparison analysis and classification are as follows:

[0036] If LEC i <LEC min If the priority of this weighing data is low, then this weighing data is classified as low-priority weighing data.

[0037] If LEC min ≤LEC i ≤LEC max If the priority of this weighing data is medium, then this weighing data is classified as medium priority weighing data.

[0038] If LEC i >LEC max If the processing priority of this weighing data is high, then this weighing data will be classified as high-priority weighing data.

[0039] Preferably, based on the division results of each weighing data, corresponding upload and caching strategies are applied to high-priority weighing data, medium-priority weighing data, and low-priority weighing data, specifically as follows:

[0040] For low-priority weighing data with a low priority to be processed, the specific strategy is to temporarily store the data in the cache and upload it in batches after the load on the weighing system has been reduced to a preset safe range.

[0041] For medium-priority weighing data with a medium priority to be processed, the specific strategy adopted is to upload the data when the system load is within an acceptable range, and to continuously monitor the system load status during the process.

[0042] For high-priority weighing data with a high priority level to be processed, the specific strategy adopted is to upload the data immediately to quickly release cache space and reduce system load pressure.

[0043] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0044] 1. This invention, through the design of a data pressure coefficient, buffer occupancy index, and load assessment model, enables the system to analyze and monitor the load status of each weighing data point in real time during peak loading periods. The calculation of various parameters allows the system to quickly assess the processing priority of each data point, ensuring that in the event of a surge in data volume, the system can accurately identify and prioritize critical data. This multi-level load assessment and prioritization ensures that the system not only maintains stable operation during high-load periods but also meets the integrity requirements of various data points in real time, effectively avoiding data loss or delays.

[0045] 2. This invention dynamically adjusts the parameter settings of the load assessment model by monitoring the transmission rate and buffer status of the weighing system in real time. This design enables the system to optimize data priority strategies in real time according to system load conditions, thereby flexibly responding to data processing needs under different load states. This adaptive adjustment method makes the system more efficient in terms of processing speed and resource allocation. Even under conditions of large load fluctuations, it can ensure the rapid transmission of critical data and the rational use of buffer resources, maximizing system performance and reducing resource waste.

[0046] 3. This invention categorizes and processes data according to different priorities, ensuring timely data transmission while avoiding system overload, thus improving transaction transparency and system reliability. The immediate upload strategy for high-priority data effectively avoids cache backlog issues under high load, while low-priority data is uploaded in batches at appropriate times, saving system resources. Overall, this invention provides efficient and reliable data processing and resource management during peak hours, solving the data processing delays and cache pressure problems caused by increased load in existing technologies. This ensures the accuracy and integrity of weighing data, significantly improving trust between transacting parties and reconciliation efficiency. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0048] Figure 1This is a flowchart illustrating a method for collecting and processing weighing data for a silo weighbridge sales model according to the present invention. Detailed Implementation

[0049] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0050] This invention provides, for example Figure 1 The method for collecting and processing weighing data for silo weighbridge sales, as shown, specifically includes the following steps:

[0051] During peak periods when multiple vehicles are continuously loaded, when the vehicles enter the silo weighing area and are weighed, the load status information of all weighing data generated when weighing each vehicle is collected in real time, and a real-time analysis framework for the data load status is established based on the load status information of all the collected weighing data.

[0052] To achieve the goal of "real-time collection of load status information for each vehicle during peak periods when multiple vehicles are continuously loaded, as each vehicle enters the weighing area of ​​the silo and is weighed," this can be accomplished by embedding a real-time monitoring and data acquisition module into the weighing data acquisition system. First, the system automatically identifies the vehicle's weighing status and begins data acquisition upon entering the weighing area. The weighing data for each vehicle includes basic information such as weight, sensor operating status, and acquisition timestamp. By monitoring the load information of each sensor in real time, the system can obtain the instantaneous processing pressure and buffer status during the current weighing process, using this data as the "load status information" for each vehicle. This load status information can be collected and stored in real time through an event-triggered mechanism. Once a sensor detects a change in weighing data or a change in vehicle status, it immediately sends a data request, collects the load status information, and stores this information in the buffer for use by the subsequent analysis framework.

[0053] The approach to achieving "establishing a real-time analysis framework for data load status based on the load status information of all collected weighing data" is as follows: The analysis framework can be established by introducing dynamic data analysis and load modeling techniques. First, the system inputs the real-time collected load status information into the data processing module, and analyzes the data processing pressure and buffer utilization during the weighing process using data modeling tools. The system can automatically generate two types of key parameters using the load status information—data pressure coefficient and buffer occupancy index—and input these parameters into the real-time analysis framework to generate load assessment coefficients. This analysis framework can be dynamically updated according to the set load status model, using complex formulas to calculate priority recommendations for each data point. Through continuous monitoring and analysis of real-time data, the framework can automatically identify the data priority under high load conditions, thus providing a basis for subsequent grading and buffering strategies.

[0054] This setup aims to handle the high-frequency switching demands of data processing and transmission during peak loading periods, ensuring the system maintains data real-time performance and integrity even under high load. By collecting load status information from weighing data in real time, the system can effectively acquire system load data generated during each vehicle's weighing, avoiding issues such as data cache overload and processing delays caused by excessive data inflow. This real-time analysis framework helps the system prioritize data using load assessment coefficients when the load surges, effectively reducing system load and ensuring priority transmission of important data. In summary, this solution implements efficient load management and prioritization strategies through software, thereby resolving data latency and incompleteness issues caused by insufficient system processing speed and cache capacity. This ensures the accuracy and real-time performance of weighing data during peak periods, enhancing the transparency and trustworthiness of the transaction process.

[0055] Real-time acquisition of processing load information for each weighing data point, followed by analysis to generate data pressure coefficient and cache occupancy index for each weighing data point;

[0056] In this embodiment, the processing load information of each weighing data is acquired in real time and analyzed after acquisition to generate the data pressure coefficient and cache occupancy index of each weighing data. The specific steps include:

[0057] The processing load information of each weighing data point is acquired in real time and preprocessed after acquisition.

[0058] Real-time acquisition of the processing load information for each weighing data point can be achieved through an embedded data acquisition system combined with a sensor monitoring module. The system uses a data listener positioned between the weighing equipment and the central processing unit to monitor key parameters of each weighing data point during transmission and processing, such as instantaneous data flow, processing time interval, and transmission rate. The system sets a specific sampling frequency to ensure that the processing load information of each weighing data point is recorded immediately upon entering the system. To achieve real-time performance, the data listener is configured in event-driven mode, capturing load information as soon as data changes are detected and quickly storing the data in a central buffer via a buffering mechanism for subsequent processing.

[0059] Preprocessing is essential to ensure data consistency and accuracy in subsequent analysis, and to reduce interference from noise and outliers. The main components of preprocessing include data cleaning, format normalization, and anomaly detection. Data cleaning removes noise, such as extreme or missing values; format normalization ensures consistent data formats for unified analysis; and anomaly detection identifies sudden changes or data anomalies. In practice, the system first filters the fluctuation range of each data point in the load information, removing extreme values ​​exceeding a preset range. Then, all data is converted to standard units and formats (e.g., time is standardized to seconds, and data flow to bytes per second). After cleaning and normalization, the system performs a smoothing algorithm on each data point to remove drastic fluctuations within a short period, ensuring data stability. This preprocessed load information is more reliable and suitable for subsequent calculations of data pressure coefficients and cache utilization indices.

[0060] Extract data pressure information and cache status information from the processing load information of each preprocessed weighing data;

[0061] Extracting data pressure information and cache status information from the processing load information of each preprocessed weighing data entry can be achieved by setting up data filters and a label matching mechanism. First, the system sets filters in the preprocessed dataset, filtering out key fields belonging to data pressure information and cache status information based on specific data labels of the load information (e.g., "data flow," "processing interval," "cache utilization rate," etc.). The data filters then break down the processing load information of each weighing data entry into different information categories, and according to predefined label rules, classify data pressure-related information (such as data flow and processing interval) as data pressure information, and cache-related data (such as cache remaining capacity and utilization rate) as cache status information. In practice, the filters automatically store this filtered information into their respective temporary data buffers, generating subsets of data pressure information and cache status information respectively, and storing them in categories, providing an efficient and clear data source for subsequent calculations of data pressure coefficients and cache occupancy indices.

[0062] The data pressure information and cache status information in the processing load information of each extracted weighing data are analyzed to generate the data pressure coefficient and cache occupancy index for each weighing data.

[0063] In this embodiment, the logic for obtaining the data pressure coefficient of each weighing data point is as follows:

[0064] Extract the data pressure information from the processing load information of each preprocessed weighing data point. Specifically, this includes the amount of data received by the weighing system at different times within a given period, the time difference between processing each weighing data point, and the rate at which the weighing system transmits each weighing data point to the central server. These are then calibrated as follows: and This represents the amount of data received by the weighing system at time m within a certain time period, specifically the amount of data from the i-th weighing data point. This represents the time difference in which the weighing system processes the i-th weighing data at time m within a certain time period. This represents the rate at which the weighing system transmits the i-th weighing data to the central server at time m within a certain time period, where i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., g, and k and g are both positive integers;

[0065] Extracting data pressure information from the processing load information of each preprocessed weighing data point can be achieved through data filtering and grouping algorithms. First, the system sets up a data filter to identify key fields of data pressure information based on preset labels, filtering out relevant information such as data volume, time difference, and transmission rate. The data filter scans the preprocessed dataset, categorizing data items that meet the criteria as "data pressure information." To achieve efficient extraction, the system can employ a timestamp indexing and label matching mechanism to label each data item in each weighing data point. The system groups data by time period, extracting the instantaneous data volume and processing time of each weighing data point at different time points from the data stream, while simultaneously recording transmission rate information. This data is automatically assigned to the "data pressure information" module for subsequent calculation of the data pressure coefficient.

[0066] These quantitative data are acquired through the real-time monitoring and data recording modules of the weighing system. Specifically, the weighing system embeds sensors or data measurement components during data reception, processing, and transmission. The system automatically records the instantaneous data volume of each incoming weighing data point, measured in bytes, and updates it in real time according to the size of the weighing data. Simultaneously, the system records the processing time interval—the time difference required to process each data point—using an internal clock to ensure an accurate reflection of the time consumed in processing each data point. Furthermore, when data is transmitted to the central server, the system monitors the data rate of the transmission channel, automatically recording the transmission rate data in bytes per second. Through these real-time monitoring and recording methods, the system can obtain the instantaneous data volume, processing time, and transmission rate of each weighing data point within different time periods, thus providing detailed basic data for the extraction and analysis of data pressure information.

[0067] The pressure coefficient for each weighing data point is calculated using the following formula:

[0068]

[0069] In the formula, DPC i Let be the data pressure coefficient of the i-th weighing data.

[0070] This calculation formula is used to comprehensively measure the processing pressure of the weighing system for each weighing data point over different time periods. First, it involves logarithmic calculations. This effectively smooths the ratio between instantaneous data flow and transmission rate, ensuring that the impact on stress is more significant when data flow is high or transmission rate is low. This non-linear processing method amplifies the stress under high load, making the formula more sensitive to changes in data stress. Secondly, square root calculation... The processing time interval is used to smooth out the impact of processing time, ensuring that shorter processing times do not significantly increase data load, but that longer processing times result in a stronger load response. The summation operation accumulates data from different time periods, reflecting the overall load across multiple time periods and ensuring that the calculation results comprehensively reflect the load across all time periods. Standardization coefficients. This balances the impact of different time periods, allowing the results to more accurately represent the average pressure of each weighing data point. These calculation steps together constitute the calculation of the data pressure coefficient, ensuring that the results are both real-time and accurately reflect the data load pressure of the weighing system during peak periods.

[0071] The data pressure coefficient DPC of the i-th weighing data iThe magnitude of the data pressure coefficient directly reflects the processing pressure on the weighing system at different time periods. A larger value indicates that the data consumes more system resources during transmission and processing, leading to increased system load. Therefore, when the data pressure coefficient is high, the system prioritizes processing that weighing data to prevent further backlog and increased system load. This priority evaluation mechanism ensures that high-pressure data is processed as early as possible, thereby reducing the overall system load, optimizing resource allocation, and achieving efficient data flow management. Conversely, for weighing data with a lower pressure coefficient, the system can schedule its processing under lower load conditions to avoid increasing system pressure during high load periods.

[0072] In this embodiment, the logic for obtaining the cache usage index of each weighing data is as follows:

[0073] Extract the cache status information from the processing load information of each preprocessed weighing data point. Specifically, this includes the number of times the weighing system processed each weighing data point at different times within a historical period, as well as the remaining cache space and the percentage of space used at different times within the period, and label them accordingly. CR m and UC m , CR represents the number of times the weighing system processes the i-th weighing data at time n within a historical period. m This represents the remaining space in the weighing system cache at time m within a certain time period, UC m This represents the percentage of the weighing system buffer used at time m within a certain time period, where i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., g, n = 1, 2, 3, ..., h, and k, g, and h are all positive integers.

[0074] Extracting cache status information from the processing load information of each preprocessed weighing data point can be achieved through timestamp segmentation and data filtering. First, the system sets filters in the preprocessed dataset, dividing the data according to timestamps to extract the required cache status information from various points in time over a historical period. The system filters out key fields related to cache status, such as the number of data processing iterations, remaining cache space, and cache usage ratio. Specifically, the system groups data by time period, uses filters to locate and mark data items that meet the criteria, categorizes the cache status information of each weighing data point into a separate dataset, and assigns it to the cache status information module. In this way, the cache status information of each weighing data point can be effectively categorized and stored, providing a clear data foundation for subsequent calculation of the cache occupancy index.

[0075] These quantitative data are collected and recorded in real time by the monitoring module in the weighing system. Specifically, the number of data processing times (i.e., data processing frequency) is the total number of times the system processes each piece of weighing data over a historical period. The system automatically counts each processing session using an internal counter and records the cumulative number of processing sessions for each piece of data. The remaining cache space is the remaining capacity (in bytes) of the weighing system cache recorded at different times. The system's cache monitoring program automatically detects the available cache space at each sampling time and stores it in the log. The cache usage ratio is the percentage of used cache space relative to the total cache capacity. The system calculates the cache usage ratio by comparing the total cache capacity with the remaining capacity. All this data is automatically recorded in the system and stored over time, forming a time-series dataset for subsequent cache status information extraction and cache occupancy index calculation.

[0076] The buffer usage index for each weighing data point is calculated using the following formula:

[0077]

[0078] In the formula, COI i Let be the cache usage index for the i-th weighing data.

[0079] This formula comprehensively reflects the occupancy of each weighing data point in the cache resources through various non-linear calculation methods, thereby accurately assessing the system's cache pressure. The formula... The inverse of the remaining cache capacity is taken, so that the value of this item is higher when the cache capacity is lower, thereby amplifying the impact of cache stress on the cache usage index. This helps to prioritize the processing of high cache usage data when cache space is insufficient. By taking the cube and square root of cache utilization, the non-linear effect of cache utilization is enhanced, making the impact on the exponent more significant as cache utilization increases, thus enabling the system to respond more sensitively to cache tightness. Part Three Taking the logarithm of the data processing frequency smooths out the impact of frequency, preventing excessive fluctuations in the index caused by high-frequency data processing, while still amplifying the effect of high-frequency data on system cache usage. By summing and averaging data from different time periods, the formula integrates the cache status at different time points, ensuring that the calculated cache usage index reflects instantaneous pressure while possessing smoothness and accuracy, enabling the system to better manage data priority and allocate cache resources.

[0080] The cache usage index (COI) of the i-th weighing data iThe magnitude of the cache occupancy index directly reflects the extent to which the data consumes system cache resources. A higher index indicates that the data is occupying more resources in the cache and has a greater impact on system cache pressure. Therefore, when the cache occupancy index is high, the system will prioritize processing the weighing data to free up cache space, reduce system cache pressure, and ensure that cache resources do not become scarce due to prolonged occupation. In this way, by prioritizing the processing of data with high cache occupancy indices, the allocation of system resources can be optimized, preventing the cache space from being filled with a large amount of unprocessed data, and helping the system maintain smooth cache management under high load.

[0081] A load assessment model is constructed based on the data pressure coefficient and buffer usage index of each generated weighing data, and the load assessment coefficient of each weighing data is generated. After generation, the model is analyzed to assess the processing priority of each weighing data and classify each weighing data into three categories: high-priority weighing data, medium-priority weighing data, and low-priority weighing data based on the assessment results.

[0082] In this embodiment, a load assessment model is constructed based on the data pressure coefficient and buffer usage index of each generated weighing data, and the load assessment coefficient of each weighing data is generated. The specific steps include:

[0083] Collect the data pressure coefficient, buffer usage index, and corresponding load assessment coefficient of several weighing data points generated over a period of time, and label them as follows: and x represents the number of the data pressure coefficient, buffer usage index and corresponding load assessment coefficient of several weighing data generated in the past period, x = 3, 4, 5, ..., d, where d is a positive integer, and the collected data in the past period are formed into a historical dataset;

[0084] The system can collect data pressure coefficients, cache occupancy indices, and corresponding load assessment coefficients for each weighing data point generated over a past period by setting up an automatic data recording module and a time-series database. The system's data recording module automatically records each generated data pressure coefficient, cache occupancy index, and load assessment coefficient, adding a timestamp for traceability and analysis. At the moment of data generation, the system stores these coefficients in real-time in the time-series database, organizing them into structured records in chronological order. This database supports regular backups and retrieval to ensure the integrity and accessibility of historical data. By querying data periodically or within custom time windows (such as the past 24 hours, 7 days, or one month), the system can easily obtain various coefficient data for a specified time period, thus providing a sufficient data foundation for building the load assessment model.

[0085] Limiting the value of x to a positive integer greater than or equal to 3 is primarily to ensure data sufficiency, allowing for the accurate calculation of the three regression coefficients. The objective equation contains three regression coefficients (β0, β1, and β2), thus requiring at least three independent equations to form a system of equations to uniquely determine their values. Furthermore, limiting x to ≥ 3 not only satisfies basic mathematical requirements but also provides a more robust data foundation for regression analysis. Increasing the sample size also improves the model's accuracy and stability, reduces errors caused by fluctuations in individual data points, and ensures that the regression coefficients better reflect the actual situation, guaranteeing the reliability and generalization ability of the load assessment model under different load conditions.

[0086] A multiple regression model was chosen as the load assessment model, and it was trained using historical datasets to determine the values ​​of the regression coefficients, based on the formula:

[0087]

[0088] In the formula, β0, β1, and β2 are regression coefficients;

[0089] Multiple regression is a statistical method used to analyze the relationship between multiple independent variables (predictors) and a dependent variable (outcome variable). In load assessment models, multiple regression is chosen because it comprehensively considers the contributions of multiple influencing factors (i.e., data stress coefficient and buffer occupancy index) to the load assessment coefficient, thus more accurately evaluating the processing priority of each weighing data point. Training with historical datasets allows for the determination of the specific impact of each factor based on past load patterns, thereby optimizing the model's predictive ability. The three regression coefficients (β0, β1, and β2) in the formula represent different meanings: β0 is a constant term, reflecting the baseline load assessment value when all factors are zero; β1 represents the influence of the data stress coefficient on the load assessment coefficient; and β2 represents the weight of the buffer occupancy index on the load assessment. Through training, the model can find the optimal coefficient values, enabling more accurate and reliable prediction of the load status of each weighing data point under different load conditions.

[0090] By minimizing the error between the predicted and actual values, the regression coefficients are optimized, and the values ​​of the regression coefficients β0, β1, and β2 are finally determined.

[0091] Optimizing regression coefficients by minimizing the error between predicted and actual values ​​aims to make the model's predictions closer to reality, thereby improving the accuracy and reliability of the load assessment model. Specifically, this is achieved by using the least squares method or other optimization algorithms to compare the actual load assessment values ​​from historical datasets with the model's predicted values, calculating the sum of squared errors, and finding its minimum. During this process, the model continuously adjusts the regression coefficients β0, β1, and β2 until the error is minimized. This process ensures that the regression coefficients fully reflect the actual impact of data pressure coefficients and buffer occupancy indices on the load. The resulting regression coefficients provide more accurate load assessments under different load conditions, thus better guiding data prioritization and processing.

[0092] Using the finalized regression coefficients, the data pressure coefficients (DPC) of each real-time generated weighing data point are input into the constructed load assessment model. i and Cache Usage Index (COI) i The load assessment coefficient (LEC) for each weighing data point is generated in real time. i .

[0093] In this embodiment, the load assessment coefficient (LEC) of each generated weighing data is used. i Compared with the pre-set load assessment coefficient threshold range [LEC] min LEC max The data is compared, and the priority of each weighing data point is assessed based on the comparison results. Based on the assessment results, each weighing data point is divided into three categories: high-priority weighing data, medium-priority weighing data, and low-priority weighing data. The specific comparison analysis and classification are as follows:

[0094] If LEC i <LEC min If the priority of this weighing data is low, then this weighing data is classified as low-priority weighing data.

[0095] This situation indicates that the data has low demand on system resources, meaning its data pressure coefficient and cache usage index are both at low levels. This type of data will not significantly put pressure on the system's processing load and cache resources, and therefore can be classified as low-priority weighing data. In this case, the system will choose to delay processing or process it when resources are idle, thus prioritizing the allocation of resources to data with higher loads, achieving optimized allocation of system resources.

[0096] If LEC min ≤LEC i ≤LEC max If the priority of this weighing data is medium, then this weighing data is classified as medium priority weighing data.

[0097] This situation indicates that the data's demand for system resources is at a moderate level, meaning its data pressure coefficient and cache usage index are both moderate. This type of data is considered medium-priority weighing data, meaning the system needs to process it at an appropriate time, rather than immediately. This priority setting helps the system flexibly schedule resources under load balancing, ensuring that medium-load data is processed promptly while avoiding impacting the processing efficiency of high-priority data.

[0098] If LEC i >LEC max If the processing priority of this weighing data is high, then this weighing data will be classified as high-priority weighing data.

[0099] This situation indicates that the data places a high demand on system resources, potentially reaching peak levels in both data pressure and cache usage. This type of data is categorized as high-priority weighing data, meaning that neglecting to process it could exacerbate system load, leading to cache strain or processing delays. Therefore, the system prioritizes processing this type of data to quickly release resources, alleviate system pressure, and prevent the high-load condition from impacting overall system performance.

[0100] The "pre-defined load assessment coefficient threshold range" can be determined through historical data analysis and statistical methods. First, the system collects a large amount of historical load assessment coefficient data generated under different load conditions, constructing a time-series dataset. Then, the software uses statistical analysis tools to process this dataset and calculate the LEC (Load Assessment Coefficient). i The mean, standard deviation, and distribution of the data can be determined using cluster analysis or quantile methods, such as selecting the 25th and 75th quantiles, or by defining the LEC by setting an interval one to two standard deviations away from the mean. min and LEC max The threshold is determined by a threshold range. This method ensures that the threshold range reflects the true stress level of the system under different load conditions, while also adapting to fluctuations in system resources. The software automatically generates and adjusts the threshold based on this analysis process to ensure its adaptability and rationality to real-time load conditions, thereby optimizing resource allocation strategies.

[0101] Based on the division results of each weighing data, corresponding upload and caching strategies are implemented for high-priority weighing data, medium-priority weighing data and low-priority weighing data respectively;

[0102] In this embodiment, based on the division results of each weighing data, corresponding upload and caching strategies are applied to high-priority weighing data, medium-priority weighing data, and low-priority weighing data, respectively, as follows:

[0103] For low-priority weighing data with a low priority to be processed, the specific strategy is to temporarily store the data in the cache and upload it in batches after the weighing system load drops to a preset safe range, so as to reduce resource consumption under high system load.

[0104] For low-priority weighing data, the processing can be achieved through the collaborative work of the cache management module and the load monitoring module. The system first temporarily stores the low-priority data in a cache, and the load monitoring module monitors the load status of the weighing system in real time. When the load monitoring module detects that the system load has decreased to a preset safe range, the cache management module automatically triggers a batch upload operation, uploading the cached low-priority data uniformly. This design aims to avoid frequent uploads of low-priority data under high system load conditions, reducing system resource consumption and thus ensuring the real-time processing capability of high-priority data.

[0105] For medium-priority weighing data with a medium priority to be processed, the specific strategy adopted is as follows: upload the data when the system load is within an acceptable range, ensure that the data is transmitted within a reasonable time, and continuously monitor the system load status during this process so as to dynamically adjust the upload frequency as needed;

[0106] For medium-priority weighing data, which requires moderate processing priority, real-time load monitoring and dynamic frequency adjustment algorithms can be used. When the system detects that the load is within an acceptable range, it will automatically schedule the upload of medium-priority data and activate the load monitoring function to continuously assess the system load status during the upload process. The system dynamically adjusts the upload frequency based on the load situation; if the load rises to near a critical point, the upload frequency will be automatically reduced; if the load lightens, the upload frequency will be gradually increased. This design is intended to ensure that medium-priority data is transmitted within a reasonable timeframe, while simultaneously ensuring that the upload process does not place additional pressure on system resources, thus maintaining a balanced load.

[0107] For high-priority weighing data with a high priority level to be processed, the specific strategy adopted is to upload the data immediately to quickly release cache space, reduce system load pressure, and ensure the stability and real-time performance of the system under high load conditions.

[0108] For high-priority weighing data with a high priority level, priority queue management and an instant upload mechanism can be used. The system creates a priority queue for high-priority data. Once data is generated, it immediately enters this queue and triggers an instant upload operation, ensuring that high-priority data is uploaded to the central server with the highest priority. This process skips cache storage and proceeds directly to transmission to reduce processing latency. Simultaneously, the system dynamically releases cache space to ensure that the next batch of high-priority data can be processed promptly. The purpose of this design is to quickly alleviate system pressure under high load conditions, ensuring the real-time performance of high-priority data and system stability.

[0109] The system continuously monitors the transmission rate and buffer status information, and dynamically adjusts the parameter settings of the load assessment model based on real-time analysis results to ensure the integrity and real-time performance of weighing data during peak loading periods.

[0110] A real-time monitoring module can be configured to continuously monitor system transmission rate and cache status information. This module periodically acquires the current transmission rate (bytes / second) and cache status information (including remaining cache capacity and utilization), and automatically records this data in a time-series database for real-time analysis. The monitoring module uses an event-triggered mechanism or fixed sampling intervals to ensure that the system can capture subtle changes in transmission and cache status during peak data load periods. The purpose of this is to ensure real-time monitoring of transmission and cache pressure, facilitating rapid identification of anomalies and initiation of corrective measures to maintain system stability.

[0111] The system utilizes dynamic analysis algorithms to analyze real-time monitoring data. By calculating the magnitude of changes in transmission rate, the trend of cache utilization, and the rate of decrease in remaining cache capacity, it generates a load trend analysis report. This analysis module can determine whether the current system load status is abnormal based on set thresholds. If the transmission rate continues to decrease or the cache utilization continues to increase, the system will mark it as a high-load state and trigger a load adjustment alert. The purpose of dynamic analysis is to help the system predict load trends, enabling the load assessment model to quickly adapt to changes, ensuring timely identification and response to load pressure during peak periods, and reducing the risk of data loss.

[0112] When the system detects abnormal load conditions, it automatically adjusts the parameter settings of the load assessment model. Based on the analysis results, the system automatically optimizes key parameters of the load assessment model (such as regression coefficients or threshold ranges) to adapt to the current transmission and buffering status. For example, under high load conditions, the weight of high-priority data can be appropriately increased or the priority threshold can be tightened, allowing the system to prioritize high-load data and reduce the processing frequency of low-priority data. The purpose of this is to ensure that, through dynamic parameter adjustment, the system can concentrate resources on processing critical data under increased load pressure, guaranteeing the integrity and real-time nature of weighing data during peak periods and avoiding delays or data loss caused by load fluctuations.

[0113] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0114] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0115] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0116] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0117] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0118] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0119] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0120] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for collecting and processing weighing data for silo weighbridge sales, characterized in that, Specifically, the following steps are included: During peak periods when multiple vehicles are continuously loaded, when the vehicles enter the silo weighing area and are weighed, the load status information of all weighing data generated when weighing each vehicle is collected in real time, and a real-time analysis framework for the data load status is established based on the load status information of all the collected weighing data. Real-time acquisition of processing load information for each weighing data point, followed by analysis to generate data pressure coefficient and cache occupancy index for each weighing data point; The logic for obtaining the data pressure coefficient of each weighing data point is as follows: Extract the data pressure information from the processing load information of each preprocessed weighing data point. Specifically, this includes the amount of data received by the weighing system at different times within a given period, the time difference between processing each weighing data point, and the rate at which the weighing system transmits each weighing data point to the central server. These are then calibrated as follows: , and , Indicates a period of time The weighing system receives the first The amount of data in each weighing data point Indicates a period of time The weighing system processes the first The time difference between the weighing data points Indicates a period of time The weighing system will be the first The rate at which weighing data is transmitted to the central server. , , and All are positive integers; The pressure coefficient for each weighing data point is calculated using the following formula: In the formula, For the first The pressure coefficient of the weighing data; The logic for obtaining the cache usage index of each weighing data point is as follows: Extract the cache status information from the processing load information of each preprocessed weighing data point. Specifically, this includes the number of times the weighing system processed each weighing data point at different times within a historical period, as well as the remaining cache space and the percentage of space used at different times within the period, and label them accordingly. , and , Indicates a period of time in history The weighing system at all times for the first The number of times the weighing data is processed. Indicates a period of time Continuously weigh the remaining space in the system cache. Indicates a period of time The used percentage of the system's cache is constantly being weighed. , , , , and All are positive integers; The buffer usage index for each weighing data point is calculated using the following formula: In the formula, For the first The cache usage index of each weighing data item; A load assessment model is constructed based on the data pressure coefficient and buffer usage index of each generated weighing data, and the load assessment coefficient of each weighing data is generated. After generation, the model is analyzed to assess the processing priority of each weighing data and classify each weighing data into three categories: high-priority weighing data, medium-priority weighing data, and low-priority weighing data based on the assessment results. Based on the division results of each weighing data, corresponding upload and caching strategies are implemented for high-priority weighing data, medium-priority weighing data and low-priority weighing data respectively; The system continuously monitors the transmission rate and buffer status information, and dynamically adjusts the parameter settings of the load assessment model based on real-time analysis results.

2. The method for collecting and processing weighing data for silo weighbridge sales as described in claim 1, characterized in that, The processing load information of each weighing data point is acquired in real time and analyzed to generate the data pressure coefficient and cache occupancy index for each weighing data point. This process includes the following steps: The processing load information of each weighing data point is acquired in real time and preprocessed after acquisition. Extract data pressure information and cache status information from the processing load information of each preprocessed weighing data; The data pressure information and cache status information in the processing load information of each extracted weighing data are analyzed to generate the data pressure coefficient and cache occupancy index for each weighing data.

3. The method for collecting and processing weighing data for silo weighbridge sales as described in claim 2, characterized in that, A load assessment model is constructed based on the data pressure coefficient and buffer usage index of each generated weighing data point, generating load assessment coefficients for each weighing data point. This process includes the following steps: Collect the data pressure coefficient, buffer usage index, and corresponding load assessment coefficient of several weighing data points generated over a period of time, and label them as follows: , and , This represents the data pressure coefficient, cache usage index, and corresponding load assessment coefficient number of several weighing data points generated over a past period. , The value is a positive integer, and the collected data over a period of time will be used to form a historical dataset; A multiple regression model was chosen as the load assessment model, and it was trained using historical datasets to determine the values ​​of the regression coefficients, based on the formula: In the formula, , and These are the regression coefficients; By minimizing the error between the predicted and actual values, the regression coefficients are optimized and finally determined. , and The possible values ​​of ; Using the finalized regression coefficients, the data pressure coefficients of each real-time generated weighing data point are input into the constructed load assessment model. and cache usage index Real-time generation of load assessment coefficients for each weighing data point .

4. The method for collecting and processing weighing data for silo weighbridge sales as described in claim 3, characterized in that, The load assessment coefficient of each generated weighing data point Compared with the pre-set load assessment coefficient threshold range A comparison is performed, and the processing priority of each weighing data point is assessed based on the comparison results. Based on the assessment results, each weighing data point is divided into three categories: high-priority weighing data, medium-priority weighing data, and low-priority weighing data. The specific comparison analysis and classification are as follows: like If the priority of this weighing data is low, then this weighing data is classified as low-priority weighing data. like If the priority of this weighing data is medium, then this weighing data is classified as medium priority weighing data. like If the processing priority of this weighing data is high, then this weighing data will be classified as high-priority weighing data.

5. The method for collecting and processing weighing data for silo weighbridge sales as described in claim 4, characterized in that, Based on the classification of each weighing data entry, corresponding upload and caching strategies are implemented for high-priority, medium-priority, and low-priority weighing data, as follows: For low-priority weighing data with a low priority to be processed, the specific strategy is to temporarily store the data in the cache and upload it in batches after the load on the weighing system has been reduced to a preset safe range. For medium-priority weighing data with a medium priority to be processed, the specific strategy adopted is to upload the data when the system load is within an acceptable range, and to continuously monitor the system load status during the process. For high-priority weighing data with a high priority level to be processed, the specific strategy adopted is to upload the data immediately to quickly release cache space and reduce system load pressure.

Images