Weighing data acquisition and processing method for silo wagon balance sales mode

Abstract

The invention discloses a weighing data acquisition and processing method for a silo wagon balance sales mode, and relates to the technical field of weighing data acquisition and processing, and the method specifically comprises the following steps: obtaining the processing load information of each piece of weighing data in real time, carrying out the analysis after obtaining, and obtaining the processing load information of each piece of weighing data; respectively generating a data pressure coefficient and a cache occupation index of each piece of weighing data; constructing a load evaluation model for the data pressure coefficient and the cache occupation index of each piece of generated weighing data, generating a load evaluation coefficient of each piece of weighing data, performing analysis after generation, evaluating the to-be-processed priority degree of each piece of weighing data, and calculating the to-be-processed priority degree of each piece of weighing data; and according to an evaluation result, dividing each piece of weighing data into high-priority weighing data, middle-priority weighing data and low-priority weighing data. According to the invention, the problems of real-time processing and cache management of weighing data in a high-load state are solved, and the integrity of data transmission and efficient distribution of system resources are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of weighing data collection and processing, and in particular to a weighing data collection and processing method for a silo floor scale sales model. Background Art

[0002] In the modern coal mine production and logistics system, an efficient and accurate loading and weighing process is crucial to ensuring the fairness of bulk goods transactions such as coal, improving transportation efficiency and optimizing inventory management. The silo scale sales model refers to the real-time weighing of the loaded goods through the scale installed in the silo during the transaction of bulk goods such as coal, so as to achieve instant confirmation of the quantity of goods by both parties to the transaction. This model not only improves the transparency of transactions, but also promotes the efficient operation of logistics. Therefore, the systematic collection and processing of weighing data for the silo scale sales model can ensure the timely update and accurate recording of weighing information, provide reliable data support for all parties to the transaction, and further optimize the overall logistics management and decision-making process.

[0003] The existing weighing data collection and processing technology for the silo scale sales model usually includes multiple links. First, the scale system monitors the weight of the loading vehicle in real time through sensors. When the vehicle enters the weighing area, the sensor automatically records its weight information and transmits this information to the central processing system in real time through the data acquisition device. During the data transmission process, the system will perform a preliminary verification of the data to ensure its accuracy. Next, the data processing software will analyze and store the collected weighing data, and associate it with other relevant information (such as loading time, vehicle number and cargo type) to form a complete weighing record. Finally, these data are integrated into the logistics management system for real-time query and reconciliation by both parties to the transaction, providing support for subsequent transaction decisions and inventory management.

[0004] The prior art has the following deficiencies:

[0005] During peak hours when multiple vehicles are loaded continuously, different vehicles need to frequently enter the weighing area and record their respective weight data in real time due to different loads. Under this high-load state, the data processing and transmission requirements of the weighing system of the scale increase sharply. The high-frequency switching of processing and transmission tasks leads to a significant increase in the system load, which is prone to delays in weighing data recording or failure to store some data in time. Since the frequent loading operation requires the weighing system to continuously process a large amount of data in a short period of time, and the processing speed and data cache capacity of the existing technology are insufficient, the system cannot ensure that the weighing data of each vehicle is recorded and uploaded to the central server in time, resulting in the loss of real-time data and reduced accuracy. As a result, data delays or incomplete records directly lead to inaccurate records of the loading quantity, affecting the transparency and fairness of the transaction, and the transaction parties may find inconsistent data when reconciling accounts, thereby increasing the reconciliation cost and extending the reconciliation time, which may further cause transaction disputes and affect the trust relationship between the two parties.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide a weighing data collection and processing method for a silo scale sales model to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a weighing data collection and processing method for a silo scale sales model, specifically comprising the following steps:

[0009] During the peak period of continuous loading of multiple vehicles, 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] Acquire the processing load information of each weighing data in real time, analyze it after acquisition, and generate the data pressure coefficient and cache occupancy index of each weighing data;

[0011] A load assessment model is constructed for the data pressure coefficient and cache occupancy index of each weighing data generated, and a load assessment coefficient of each weighing data is generated. After the generation, the load assessment coefficient is analyzed to assess the priority of each weighing data to be processed, and each weighing data is divided into three categories according to the assessment results: high priority weighing data, medium priority weighing data and low priority weighing data;

[0012] According to the classification results of each weighing data, corresponding upload and cache strategies are respectively implemented for high priority weighing data, medium priority weighing data and low priority weighing data;

[0013] Continuously monitor the system's transmission rate and cache status information, and dynamically adjust the parameter settings of the load assessment model based on real-time analysis results.

[0014] Preferably, the processing load information of each weighing data is obtained in real time, and analyzed after being obtained to generate a data pressure coefficient and a cache occupancy index for each weighing data, which specifically includes the following steps:

[0015] Obtain the processing load information of each weighing data in real time and perform preprocessing after acquisition;

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

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

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

[0019] The data pressure information in the processing load information of each piece of weighing data after preprocessing is extracted, including the amount of data received by the weighing system at different times within a period of time, the time difference of processing each piece of weighing data by the weighing system, and the rate at which the weighing system transmits each piece of weighing data to the central server, and is calibrated as and It indicates the amount of weighing data received by the weighing system at time m within a period of time. It represents the time difference of the weighing system processing the i-th weighing data at time m within a period of time. represents the rate at which the weighing system transmits the i-th weighing data to the central server at time m within a period of time, i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., g, k and g are both positive integers;

[0020] Calculate the data pressure coefficient of each weighing data. The specific calculation formula is as follows:

[0021]

[0022] Where DPC i is the data pressure coefficient of the i-th weighing data.

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

[0024] Extract the cache status information from the processing load information of each piece of weighing data that has been preprocessed, including the number of times the weighing system processes each piece of weighing data at different times in a historical period, and the remaining space and used ratio value of the weighing system cache at different times in a period of time, and mark them as CR m and UC m , Indicates the number of times the weighing system processes the i-th weighing data at time n in a historical period of time. CR m Indicates the remaining space of the weighing system cache at time m within a period of time, UC m Indicates the used proportion value of the weighing system cache at time m within a period of time, i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., g, n = 1, 2, 3, ..., h, k, g and h are all positive integers;

[0025] Calculate the cache occupancy index of each weighing data. The specific calculation formula is as follows:

[0026]

[0027] In the formula, COI i is the cache occupancy index of the i-th weighing data.

[0028] Preferably, a load assessment model is constructed for the data pressure coefficient and cache occupancy index of each piece of weighing data generated to generate a load assessment coefficient for each piece of weighing data, specifically comprising the following steps:

[0029] The data pressure coefficient, cache occupancy index and corresponding load assessment coefficient of several weighing data generated in the past period of time are collected and calibrated as and x represents the data pressure coefficient, cache occupancy index and corresponding load assessment coefficient number of each weighing data generated in the past period of time, x=3, 4, 5, ..., d, d is a positive integer, and the data collected in the past period of time is formed into a historical data set;

[0030] Select the multivariate regression model as the load assessment model, and train it through the historical data set to determine the value of the regression coefficient according to the formula:

[0031]

[0032] Where β0, β1 and β2 are regression coefficients;

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

[0034] Using the final determined regression coefficient, the constructed load assessment model is input with the data pressure coefficient DPC of each weighing data generated in real time. i and cache occupancy index COI i , generate the load evaluation coefficient LEC of each weighing data in real time i .

[0035] Preferably, the load evaluation coefficient LEC of each weighing data generated is i and the pre-set load evaluation factor threshold interval [LEC min , LEC max ] to compare, evaluate the priority of each weighing data to be processed according to the comparison results, and divide each weighing data into three categories according to the evaluation results: high priority weighing data, medium priority weighing data and low priority weighing data. The specific comparison analysis and division are as follows:

[0036] If LEC i <LEC min , the priority of the weighing data to be processed is low, then the weighing data is classified as low priority weighing data;

[0037] If LEC min ≤LEC i ≤LEC max , the priority of the weighing data to be processed is medium, then the weighing data is classified as medium priority weighing data;

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

[0039] Preferably, according to the division results of each weighing data, corresponding upload and cache strategies are respectively performed for high priority weighing data, medium priority weighing data and low priority weighing data, specifically:

[0040] For low-priority weighing data to be processed, the strategy adopted is: temporarily store the data in the cache, and upload it in batches after the load of the weighing system is reduced to a preset safety range;

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

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

[0043] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0044] 1. The present invention designs data pressure coefficient, cache occupancy index and load assessment model, so that the system can analyze and monitor the load status of each weighing data in real time during peak loading period. The calculation of various parameters enables the system to quickly evaluate the processing priority of each data, ensuring that when the amount of data increases sharply, the system can accurately identify key data and give priority to processing. This multi-level load assessment and priority division enables the system to not only maintain stable operation during high-load periods, but also meet the integrity requirements of various data in real time, effectively avoiding data loss or delay problems.

[0045] 2. The present invention monitors the transmission rate and cache status of the weighing system in real time, and the system can dynamically adjust the parameter settings of the load assessment model. Such a design can instantly optimize the data priority strategy according to the system load conditions, so as to flexibly respond to data processing requirements under different load conditions. This adaptive adjustment method makes the system more efficient in processing speed and resource allocation. Even in the case of large load fluctuations, it can ensure the rapid transmission of key data and the rational use of cache resources, maximize the optimization of system performance and reduce resource waste.

[0046] 3. The present invention classifies and processes data of different priorities. The solution ensures the timeliness of data transmission while avoiding system overload, thereby improving transaction transparency and system reliability. The instant upload strategy for high-priority data effectively avoids the problem of cache occupancy backlog under high-load conditions, while low-priority data is uploaded in batches at the right time, saving system resources. In general, the present invention can provide efficient and reliable data processing and resource management during peak hours, solving the problems of data processing delays and cache pressure caused by increased load in the prior art, thereby ensuring the accuracy and integrity of weighing data and significantly improving the trust and reconciliation efficiency between the two parties to the transaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0048] Figure 1The present invention is a flow chart of a weighing data collection and processing method for a silo scale sales model. DETAILED DESCRIPTION

[0049] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0050] The present invention provides Figure 1 The method for collecting and processing weighing data for a silo scale sales model shown in the figure specifically comprises the following steps:

[0051] During the peak period of continuous loading of multiple vehicles, 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] The way to achieve "during the peak period of continuous loading of multiple vehicles, when the vehicle enters the silo weighing area and is weighed, the load status information of all weighing data generated when each vehicle is weighed is collected in real time": This function can be achieved by embedding real-time monitoring and data acquisition modules in the weighing data acquisition system. First, the system will automatically identify the weighing status of the vehicle after the vehicle enters the weighing area and start data collection. The weighing data of each vehicle contains basic information such as weight value, sensor working status and acquisition timestamp. By real-time monitoring the load information of each sensor, the system can obtain the instantaneous processing pressure and cache status in the current weighing process, and use this data as the "load status information" of each vehicle. This load status information can be collected and stored in real time by establishing an event trigger mechanism. Once the sensor detects a change in weighing data or a change in vehicle status, it will immediately send a data request to collect load status information and store this information in the cache for use in the subsequent analysis framework.

[0053] The way to achieve "and establish a real-time analysis framework for data load status based on the load status information of all weighing data collected": Establishing the analysis framework can be achieved by introducing dynamic data analysis and load modeling technology. First, the system inputs the load status information collected in real time into the data processing module, and analyzes the data processing pressure and cache utilization during the weighing process through the data modeling tool. The system can use the load status information to automatically generate two types of key parameters-data pressure coefficient and cache occupancy index, and input these parameters into the real-time analysis framework to generate a load assessment coefficient. The analysis framework can be dynamically updated according to the set load status model, and a complex formula is used to calculate the priority recommendation for each data. Through continuous monitoring and analysis of real-time data, the framework can automatically identify the data priority under high load conditions, thereby providing a basis for subsequent classification and buffering strategies.

[0054] The purpose of this setting is to cope with the high-frequency switching needs of data processing and transmission during peak loading periods, and to ensure that the system can maintain the real-time and integrity of the data under high load conditions. By collecting the load status information of weighing data in real time, the system can effectively obtain the system load data generated when each vehicle is weighed, avoiding problems such as data cache overlimit and processing delays caused by excessive data inflow. This real-time analysis framework can help the system prioritize data through load assessment coefficients when the load increases suddenly, effectively reduce the system load, and ensure the priority transmission of important data. In short, the solution implements efficient load management and priority division strategies through software, thereby solving the problems of data delay and incomplete data caused by insufficient system processing speed and cache capacity, ensuring the accuracy and real-time performance of weighing data during peak hours, and improving the transparency and trust of the transaction process.

[0055] Acquire the processing load information of each weighing data in real time, analyze it after acquisition, and generate the data pressure coefficient and cache occupancy index of each weighing data;

[0056] In this embodiment, the processing load information of each weighing data is obtained in real time, and analyzed after acquisition to generate the data pressure coefficient and cache occupancy index of each weighing data, which specifically includes the following steps:

[0057] Obtain the processing load information of each weighing data in real time and perform preprocessing after acquisition;

[0058] The embedded data acquisition system combined with the sensor monitoring module can be used to obtain the processing load information of each weighing data in real time. The system sets a data listener between the weighing equipment and the central processing unit to monitor the key parameters of each weighing data in the transmission and processing process, such as instantaneous data flow, processing time interval and transmission rate. The system will set a certain sampling frequency to ensure that the processing load information of each weighing data is recorded immediately once it enters the system. In order to achieve real-time performance, the system configures the data listener in event-driven mode, captures the load information immediately when monitoring the data changes, and quickly stores the data in the central cache through the buffer mechanism for subsequent processing.

[0059] Preprocessing is performed to ensure the consistency and accuracy of data for subsequent analysis and to reduce the interference of noise and outliers on the analysis. The main contents of preprocessing include data cleaning, format normalization, and anomaly detection. Data cleaning is used to remove noise, such as extreme values ​​or missing values; format normalization ensures that the formats of each data are consistent, which is convenient for unified analysis; anomaly detection is used to identify mutation points or data anomalies in the data. In specific implementation, the system will first filter the fluctuation range of each data in the load information, remove extreme values ​​that exceed the preset range, and then convert all data into standard units and formats (for example, unify time into seconds and data flow into bytes / second). After cleaning and normalization, the system performs a smoothing algorithm on each data to remove sharp fluctuations in a short period of time and ensure the stability of the data. In this way, the preprocessed load information is more reliable and suitable for the subsequent calculation of data pressure coefficient and cache occupancy index.

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

[0061] Extracting the data pressure information and cache status information from the processing load information of each preprocessed weighing data can be achieved by setting data filters and label matching mechanisms. First, the system sets filters in the preprocessed data set to filter out key fields belonging to the data pressure information and cache status information according to specific data labels of the load information (such as "data flow", "processing time interval", "cache usage rate", etc.). The data filter splits the processing load information of each weighing data into different information categories, and classifies the data pressure-related information (such as data flow, processing time interval, etc.) as data pressure information, and the cache-related data (such as cache remaining capacity, usage rate, etc.) as cache status information according to predefined label rules. In specific implementation, the filter automatically stores the filtered information into their respective temporary data buffers, generates subsets of data pressure information and cache status information respectively, and stores them in categories, providing an efficient and clear data source for the subsequent calculation of data pressure coefficient and cache occupancy index.

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

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

[0064] The data pressure information in the processing load information of each piece of weighing data after preprocessing is extracted, including the amount of data received by the weighing system at different times within a period of time, the time difference of processing each piece of weighing data by the weighing system, and the rate at which the weighing system transmits each piece of weighing data to the central server, and is calibrated as and It indicates the amount of weighing data received by the weighing system at time m within a period of time. It represents the time difference of the weighing system processing the i-th weighing data at time m within a period of time. represents the rate at which the weighing system transmits the i-th weighing data to the central server at time m within a period of time, i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., g, k and g are both positive integers;

[0065] Extracting data pressure information from the processing load information of each preprocessed weighing data can be achieved through data filtering and grouping algorithms. First, the system sets up a data filter to identify the key fields of the data pressure information according to the preset tags, and screens out relevant information, such as data volume, time difference, and transmission rate. The data filter scans the preprocessed data set and classifies the qualified data items as "data pressure information". In order to achieve efficient extraction, the system can use timestamp indexing and label matching mechanisms to label each data item in each weighing data. The system groups according to time periods, extracts the instantaneous data volume and processing time of each weighing data at different time points from the data stream, and records the transmission rate information. These data will be automatically assigned to the "data pressure information" module for subsequent calculation of the data pressure coefficient.

[0066] These quantitative data are obtained through the real-time monitoring module and data recording module of the weighing system. Specifically, the weighing system embeds sensors or data metering components in the process of data reception, processing and transmission. The system automatically records the instantaneous data volume of each weighing data when it enters. The data volume is in bytes and is updated in real time according to the size of the weighing data. At the same time, the system records the processing time interval through the internal clock, that is, the time difference required to process each data, to ensure that the time consumption for processing each data is accurately reflected. In addition, when the data is transmitted to the central server, the system monitors the data rate of the transmission channel and automatically records the transmission rate data in bytes / 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 in different time periods, thereby providing detailed basic data for the extraction and analysis of data pressure information.

[0067] Calculate the data pressure coefficient of each weighing data. The specific calculation formula is as follows:

[0068]

[0069] Where DPC i is the data pressure coefficient of the i-th weighing data.

[0070] This calculation formula is used to comprehensively measure the processing pressure of each weighing data in different time periods on the weighing system. First, through logarithmic calculation It can effectively smooth the ratio between instantaneous data flow and transmission rate, ensuring that the impact on pressure is more significant when data flow is high or transmission rate is low. This nonlinear processing method can amplify the pressure under high load and make the formula more sensitive to changes in data pressure. Used for processing time intervals to smooth the impact of processing time, ensuring that shorter processing times do not significantly increase data pressure, but when processing time is longer, the pressure is more intense. The sum operation accumulates data from different time periods to reflect the overall pressure of multiple time periods, ensuring that the calculation result can integrate the load of each time period. Standardization coefficient The influence of different time periods is balanced, so that the result can more accurately represent the average pressure of each weighing data. These calculation steps together constitute the calculation of the data pressure coefficient, making the result both real-time and able to accurately reflect the data load pressure of the weighing system during peak periods.

[0071] Data pressure coefficient DPC of the i-th weighing data iThe size of directly reflects the processing pressure that the data generates on the weighing system in different time periods. The larger the value, the more system resources are consumed during the transmission and processing of the data, resulting in an increase in the system load. Therefore, when the data pressure coefficient is large, the system will give priority to processing the weighing data to prevent it from further backlog and increasing the system load. This priority evaluation mechanism ensures that high-pressure data can be processed as early as possible, thereby reducing the overall load of the system, optimizing resource allocation, and achieving efficient data flow management. On the contrary, for weighing data with a smaller data pressure coefficient, the system can arrange for it to be processed in a subsequent low-load state to avoid increasing system pressure under high load.

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

[0073] Extract the cache status information from the processing load information of each piece of weighing data that has been preprocessed, including the number of times the weighing system processes each piece of weighing data at different times in a historical period, and the remaining space and used ratio value of the weighing system cache at different times in a period of time, and mark them as CR m and UC m , Indicates the number of times the weighing system processes the i-th weighing data at time n in a historical period of time. CR m Indicates the remaining space of the weighing system cache at time m within a period of time, UC m Indicates the used proportion value of the weighing system cache at time m within a period of time, i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., g, n = 1, 2, 3, ..., h, k, g and h are all positive integers;

[0074] Extracting cache status information from the processing load information of each preprocessed weighing data can be achieved through timestamp segmentation and data filters. First, the system sets filters in the preprocessed data set and divides the data according to timestamps so as to extract the required cache status information from each moment in a historical period. The system will filter out key fields related to the cache status, such as the number of data processing times, the remaining cache space, and the cache usage ratio. During specific extraction, the system groups by time period, locates and marks the data items that meet the conditions through filters, and classifies the cache status information of each weighing data into a separate data set and assigns it to the cache status information module. In this way, the cache status information of each weighing data can be effectively classified and stored, providing a clear data basis for the 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 weighing data in a historical period of time. The system automatically counts each time it processes through an internal counter and records the cumulative number of times each data is processed. The remaining cache space is the remaining capacity (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 value of the cache used space to the total cache capacity. The system calculates the cache usage ratio by calculating the ratio of the total cache capacity to the remaining capacity. All these data are automatically recorded in the system and stored by time to form a time series data set for subsequent cache status information extraction and cache occupancy index calculation.

[0076] Calculate the cache occupancy index of each weighing data. The specific calculation formula is as follows:

[0077]

[0078] In the formula, COI i is the cache occupancy index of the i-th weighing data.

[0079] This formula uses a variety of nonlinear calculation methods to fully reflect the occupancy of each weighing data in the cache resources, thereby accurately evaluating the cache pressure of the system. 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 on the cache occupancy index when the cache is tight. This helps to prioritize high cache occupancy data when cache space is insufficient. By taking the cube and square root of cache utilization, the nonlinear effect of cache utilization is enhanced, making the exponential effect more significant when the cache utilization is higher, so that the system can respond more sensitively to cache tightness. Taking the logarithm of the data processing frequency smoothes the impact of the frequency and avoids excessive fluctuations in the index caused by high-frequency data processing, while still amplifying the effect of high-frequency data on the system cache occupancy. By summing and averaging the data in each time period, the formula integrates the cache status at different time points to ensure that the calculated cache occupancy index can reflect instantaneous pressure while also being smooth and accurate, allowing the system to better manage data priority and allocate cache resources.

[0080] Cache occupancy index COI of the i-th weighing data iThe size directly reflects the degree of occupation of the system cache resources by the data. The larger the index, the more resources the data occupies in the cache and the greater the impact on the system cache pressure. Therefore, when the cache occupation index is high, the system will give priority to processing the weighing data to free up cache space, reduce the system cache pressure, and ensure that cache resources will not be tightened due to long-term occupation. In this way, by giving priority to processing data with a high cache occupation index, the allocation of system resources can be optimized, avoiding the cache space being filled up by a large amount of unprocessed data, and helping the system maintain smooth cache management under high load.

[0081] A load assessment model is constructed for the data pressure coefficient and cache occupancy index of each weighing data generated, and a load assessment coefficient of each weighing data is generated. After the generation, the load assessment coefficient is analyzed to assess the priority of each weighing data to be processed, and each weighing data is divided into three categories according to the assessment results: high priority weighing data, medium priority weighing data and low priority weighing data;

[0082] In this embodiment, a load assessment model is constructed based on the data pressure coefficient and cache occupancy index of each piece of weighing data generated, and a load assessment coefficient of each piece of weighing data is generated, which specifically includes the following steps:

[0083] The data pressure coefficient, cache occupancy index and corresponding load assessment coefficient of several weighing data generated in the past period of time are collected and calibrated as and x represents the data pressure coefficient, cache occupancy index and corresponding load assessment coefficient number of each weighing data generated in the past period of time, x=3, 4, 5, ..., d, d is a positive integer, and the data collected in the past period of time is formed into a historical data set;

[0084] The data pressure coefficient, cache occupancy index and corresponding load assessment coefficient of each weighing data generated in the past period of time can be collected by setting up an automatic data recording module and a time series database. The data recording module in the system will automatically record the data pressure coefficient, cache occupancy index and load assessment coefficient generated each time, and add 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 and organizes them into structured records in chronological order. The database can support regular backup and retrieval functions to ensure the integrity and accessibility of historical data. By querying data regularly or in a custom time window (such as the past 24 hours, 7 days or a month), the system can easily obtain various coefficient data within the specified time period, thereby providing a sufficient data basis for the construction of the load assessment model.

[0085] The value of x is limited to a positive integer greater than or equal to 3, mainly to ensure data adequacy so that the three regression coefficients can be accurately calculated. There are three regression coefficients to be calculated in the target equation (β0, β1, and β2), so at least three independent equations are required to form a system of equations to uniquely determine the values ​​of these coefficients. Furthermore, limiting x ≥ 3 not only meets basic mathematical requirements, but also provides a more robust data basis for regression analysis. Increasing the data sample size can also improve the accuracy and stability of the model, reduce the errors caused by individual data fluctuations, make the regression coefficients more reflective of the actual situation, and ensure the reliability and generalization ability of the load assessment model under different load conditions.

[0086] Select the multivariate regression model as the load assessment model, and train it through the historical data set to determine the value of the regression coefficient according to the formula:

[0087]

[0088] Where β0, β1 and β2 are regression coefficients;

[0089] The multivariate regression model is a statistical method used to analyze the relationship between multiple independent variables (predictors) and a dependent variable (outcome variable). In the load assessment model, the multivariate regression model is selected because it can comprehensively consider the contribution of multiple influencing factors (i.e., data pressure coefficient and cache occupancy index) to the load assessment coefficient, so as to more accurately assess the priority of each weighing data to be processed. Through training with historical data sets, the specific impact of each factor can be determined based on past load patterns, thereby optimizing the model's prediction 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 degree of influence of the data pressure coefficient on the load assessment coefficient; and β2 represents the weight of the cache occupancy index on the load assessment. Through training, the model can find the optimal coefficient value, so that the load state of each weighing data can be more accurately and reliably predicted under different load conditions.

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

[0091] The regression coefficient is optimized by minimizing the error between the predicted value and the actual value, with the goal of making the model's prediction results closer to the actual situation, thereby improving the accuracy and reliability of the load assessment model. The specific implementation method is to use the least squares method or other optimization algorithms to compare the actual load assessment value in the historical data set with the predicted value calculated by the model, calculate the sum of squares of the error and find its minimum. In this process, the model will continuously adjust the regression coefficients β0, β1 and β2 until the error is minimized. This process ensures that the regression coefficient can fully reflect the actual impact of the data pressure coefficient and the cache occupancy index on the load. The final regression coefficient can provide more accurate load assessment under different load conditions, thereby better guiding the division and processing of data priorities.

[0092] Using the final determined regression coefficient, the constructed load assessment model is input with the data pressure coefficient DPC of each weighing data generated in real time. i and cache occupancy index COI i , generate the load evaluation coefficient LEC of each weighing data in real time i .

[0093] In this embodiment, the load evaluation coefficient LEC of each weighing data generated is i and the pre-set load evaluation factor threshold interval [LEC min , LEC max ] to compare, evaluate the priority of each weighing data to be processed according to the comparison results, and divide each weighing data into three categories according to the evaluation results: high priority weighing data, medium priority weighing data and low priority weighing data. The specific comparison analysis and division are as follows:

[0094] If LEC i <LEC min , the priority of the weighing data to be processed is low, then the weighing data is classified as low priority weighing data;

[0095] This means that the data has a low demand for system resources, that is, its data pressure coefficient and cache occupancy index are both at a low level. This type of data will not cause significant pressure on the system's processing load and cache resources, so it 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, thereby giving priority to allocating resources to data with higher loads, thereby achieving optimal allocation of system resources.

[0096] If LEC min ≤LEC i ≤LEC max , the priority of the weighing data to be processed is medium, then the weighing data is classified as medium priority weighing data;

[0097] This means that the data has a medium demand for system resources, that is, its data pressure coefficient and cache occupancy index are both moderate. This type of data is considered medium-priority weighing data, indicating that the system needs to process it at an appropriate time, but not immediately. This priority setting helps the system flexibly schedule resources under load balancing, ensuring that medium-load data is processed in a timely manner while avoiding affecting the processing efficiency of high-priority data.

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

[0099] This means that the data has a high demand for system resources and may reach a peak in both data pressure coefficient and cache occupancy index. This type of data is classified as high-priority weighing data, which means that if it is not processed first, it may increase the system load pressure, causing cache tension or processing delays. Therefore, the system will give priority to processing this type of data to quickly release resources, reduce system pressure, and prevent the high load state from affecting the overall system performance.

[0100] The "pre-set load evaluation factor threshold interval" can be determined through historical data analysis and statistical methods. First, the system collects a large amount of historical load evaluation factor data generated under different load conditions to build a time series data set. Then, the software uses statistical analysis tools to process the data set and calculate the LEC i The mean, standard deviation, and distribution of the data can be defined using cluster analysis or quantile methods, such as selecting the 25% and 75% quantiles, or by setting intervals of one to two standard deviations from the mean. min and LEC max This method ensures that the threshold interval can reflect the actual stress level of the system under different load conditions and adapt to the fluctuation of system resources. The software will automatically generate and adjust the threshold based on this analysis process to ensure its adaptability and rationality to the real-time load status and optimize the resource allocation strategy.

[0101] According to the classification results of each weighing data, corresponding upload and cache strategies are respectively implemented for high priority weighing data, medium priority weighing data and low priority weighing data;

[0102] In this embodiment, according to the classification results of each weighing data, corresponding upload and cache strategies are respectively performed for high priority weighing data, medium priority weighing data and low priority weighing data, specifically:

[0103] For low-priority weighing data to be processed, the strategy adopted is as follows: temporarily store the data in the cache, and upload it in batches when the load of the weighing system is reduced to a preset safety range, so as to reduce resource consumption under high-load conditions of the system;

[0104] For low-priority weighing data with a low priority to be processed, this can be achieved through the coordinated work of the cache management module and the load monitoring module. The system first temporarily stores the low-priority data in the cache, and monitors the load status of the weighing system in real time through the load monitoring module. When the load monitoring module detects that the system load is reduced to a preset safety range, the cache management module automatically triggers a batch upload operation and uploads the low-priority data in the cache in a unified manner. The purpose of this design is to avoid frequent uploading of low-priority data when the system is under high load, reduce the consumption of system resources, and thus ensure the real-time processing capability of high-priority data.

[0105] For the medium-priority weighing data to be processed, the specific strategy is: upload when the system load is within the 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 with a medium priority to be processed, this can be achieved through real-time load monitoring and dynamic frequency adjustment algorithms. When the system detects that the load is within an acceptable range, the system will automatically arrange to upload the medium-priority data and start the load monitoring function to continuously evaluate the system load status during the upload process. The system dynamically adjusts the upload frequency according to the load situation. If the load rises close to the critical point, the upload frequency will be automatically reduced. If the load is reduced, the upload frequency will be gradually increased. The reason for this design is that the medium-priority data needs to be transmitted within a reasonable time, but at the same time, it is necessary to ensure that the upload process does not cause additional pressure on system resources and maintain a balanced load status.

[0107] For high-priority weighing data to be processed, the specific strategy adopted is: prioritize uploading 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 to be processed, it can be achieved through priority queue management and instant upload mechanism. The system creates a priority queue for high-priority data. Once the data is generated, it enters the queue immediately and triggers the instant upload operation to ensure that the high-priority data is uploaded to the central server with the highest priority. This process skips the cache storage and directly enters the transmission to reduce processing delays. At the same time, the system will dynamically release the cache space to ensure that the next batch of high-priority data can be processed in time. The purpose of this design is to quickly reduce the system pressure under high load and ensure the real-time nature of high-priority data and the stability of the system.

[0109] Continuously monitor the system's transmission rate and cache status information, and dynamically adjust the parameter settings of the load assessment model based on real-time analysis results to ensure the integrity and real-time nature of weighing data during peak loading periods.

[0110] The real-time monitoring module can be set up to continuously monitor the system transmission rate and cache status information. The module will periodically obtain the current transmission rate (bytes / second) and cache status information (including cache remaining capacity and usage rate), and automatically record this data in the time series database for real-time analysis. The monitoring module uses event triggering mechanisms 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 that the system has real-time control of transmission and cache pressure, facilitate rapid identification of abnormal situations and initiate adjustment measures to maintain system stability.

[0111] The system uses a dynamic analysis algorithm to analyze the real-time monitoring data, and generates a load trend analysis report by calculating the change in transmission rate, the trend of cache usage, and the rate of decrease in the remaining cache capacity. The analysis module can determine whether the current system load status is abnormal based on the set threshold. If the transmission rate continues to decrease or the cache usage continues to increase, the system will mark it as a high load state and trigger a load adjustment reminder. The role of dynamic analysis is to help the system predict load trends, enable the load assessment model to quickly adapt to changes, ensure that load pressure is promptly identified and responded to during peak hours, and reduce the risk of data loss.

[0112] When the system detects an abnormal load status, it automatically adjusts the parameter settings of the load assessment model. Based on the analysis results, the system automatically optimizes the key parameters of the load assessment model (such as regression coefficients or threshold intervals) to adapt to the current transmission and cache status. For example, under high load conditions, the weight of high-priority data can be appropriately increased or the threshold of priority division can be tightened so that the system prioritizes high-load data and reduces the processing frequency of low-priority data. The purpose of this is to ensure that the system can concentrate resources to process key data when the load pressure increases through dynamic adjustment of parameters, ensure the integrity and real-time performance of weighing data during peak hours, and avoid delays or losses in data transmission due to load fluctuations.

[0113] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0114] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of 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, the process or function described in the embodiment of the present application is generated in whole or in part. 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 computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). 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 containing one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0115] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean 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 the present application.

[0116] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0117] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

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

[0119] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0120] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A weighing data collection and processing method for a silo scale sales model, characterized in that: The specific steps include: During the peak period of continuous loading of multiple vehicles, 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; Acquire the processing load information of each weighing data in real time, analyze it after acquisition, and generate the data pressure coefficient and cache occupancy index of each weighing data; A load assessment model is constructed for the data pressure coefficient and cache occupancy index of each weighing data generated, and a load assessment coefficient of each weighing data is generated. After the generation, the load assessment coefficient is analyzed to assess the priority of each weighing data to be processed, and each weighing data is divided into three categories according to the assessment results: high priority weighing data, medium priority weighing data and low priority weighing data; According to the classification results of each weighing data, corresponding upload and cache strategies are respectively implemented for high priority weighing data, medium priority weighing data and low priority weighing data; Continuously monitor the system's transmission rate and cache status information, and dynamically adjust the parameter settings of the load assessment model based on real-time analysis results.

2. The weighing data collection and processing method for the silo scale sales model according to claim 1 is characterized in that: The processing load information of each weighing data is obtained in real time, and analyzed after acquisition to generate the data pressure coefficient and cache occupancy index of each weighing data, which specifically includes the following steps: Obtain the processing load information of each weighing data in real time and perform preprocessing after acquisition; extracting data pressure information and cache status information from processing load information of each piece of preprocessed weighing data; The data pressure information and cache status information in the processing load information of each piece of weighing data extracted are analyzed to generate a data pressure coefficient and a cache occupancy index of each piece of weighing data respectively.

3. The weighing data collection and processing method for the silo scale sales model according to claim 2 is characterized in that: The logic for obtaining the data pressure coefficient of each weighing data is as follows: The data pressure information in the processing load information of each piece of weighing data after preprocessing is extracted, including the amount of data received by the weighing system at different times within a period of time, the time difference of processing each piece of weighing data by the weighing system, and the rate at which the weighing system transmits each piece of weighing data to the central server, and is calibrated as and It indicates the amount of weighing data received by the weighing system at time m within a period of time. It represents the time difference of the weighing system processing the i-th weighing data at time m within a period of time. represents the rate at which the weighing system transmits the i-th weighing data to the central server at time m within a period of time, i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., g, k and g are both positive integers; Calculate the data pressure coefficient of each weighing data. The specific calculation formula is as follows: Where DPC i is the data pressure coefficient of the i-th weighing data.

4. The weighing data collection and processing method for the silo scale sales model according to claim 3 is characterized in that: The logic for obtaining the cache occupancy index of each weighing data is as follows: Extract the cache status information from the processing load information of each piece of weighing data that has been preprocessed, including the number of times the weighing system processes each piece of weighing data at different times in a historical period, and the remaining space and used ratio value of the weighing system cache at different times in a period of time, and mark them as CR m and UC m , Indicates the number of times the weighing system processes the i-th weighing data at time n in a historical period of time. CR m Indicates the remaining space of the weighing system cache at time m within a period of time, UC m Indicates the used proportion value of the weighing system cache at time m within a period of time, i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., g, n = 1, 2, 3, ..., h, k, g and h are all positive integers; Calculate the cache occupancy index of each weighing data. The specific calculation formula is as follows: In the formula, COI i is the cache occupancy index of the i-th weighing data.

5. The weighing data collection and processing method for the silo scale sales model according to claim 4 is characterized in that: A load assessment model is constructed based on the data pressure coefficient and cache occupancy index of each piece of weighing data generated, and a load assessment coefficient of each piece of weighing data is generated, specifically including the following steps: The data pressure coefficient, cache occupancy index and corresponding load assessment coefficient of several weighing data generated in the past period of time are collected and calibrated as and x represents the data pressure coefficient, cache occupancy index and corresponding load assessment coefficient number of each weighing data generated in the past period of time, x=3, 4, 5, ..., d, d is a positive integer, and the data collected in the past period of time is formed into a historical data set; Select the multivariate regression model as the load assessment model, and train it through the historical data set to determine the value of the regression coefficient according to the formula: Where β0, β1 and β2 are regression coefficients; By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized, and finally the values ​​of the regression coefficients β0, β1 and β2 are determined; Using the final determined regression coefficient, the constructed load assessment model is input with the data pressure coefficient DPC of each weighing data generated in real time. i and cache occupancy index COI i , generate the load evaluation coefficient LEC of each weighing data in real time i .

6. A weighing data collection and processing method for a silo scale sales model according to claim 5, characterized in that: The load evaluation coefficient LEC of each weighing data generated i and the pre-set load evaluation factor threshold interval [LEC min , LEC max ] to compare, evaluate the priority of each weighing data to be processed according to the comparison results, and divide each weighing data into three categories according to the evaluation results: high priority weighing data, medium priority weighing data and low priority weighing data. The specific comparison analysis and division are as follows: If LEC i <LEC min , the priority of the weighing data to be processed is low, then the weighing data is classified as low priority weighing data; If LEC min ≤LEC i ≤LEC max , the priority of the weighing data to be processed is medium, then the weighing data is classified as medium priority weighing data; If LEC i >LEC max , the priority of the weighing data to be processed is high, then the weighing data is classified as high-priority weighing data.

7. A weighing data collection and processing method for a silo scale sales model according to claim 6, characterized in that: According to the classification results of each weighing data, corresponding upload and cache strategies are respectively implemented for high priority weighing data, medium priority weighing data and low priority weighing data, specifically: For low-priority weighing data to be processed, the specific strategy is as follows: The data is temporarily stored in the cache and uploaded in batches after the load on the weighing system is reduced to a preset safety range; For the medium-priority weighing data to be processed, the specific strategy is as follows: Upload when the system load is within the acceptable range and continuously monitor the system load status during the process; For high-priority weighing data to be processed, the specific strategy is as follows: Prioritize uploading the data immediately to quickly free up cache space and reduce system load pressure.

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