Intelligent decision-making method, device and equipment for scrap steel processing and medium

Abstract

The application relates to an intelligent decision-making method and device for scrap steel processing, equipment and medium, which are applied to the technical field of scrap steel processing. The method comprises the following steps: obtaining a scrap steel type, scrap steel basic information and real-time quotations of all steel plants in a preset range of scrap steel to be processed; calculating an expected cash price based on the real-time quotations, the scrap steel basic information and a preset cash price calculation rule; selecting a steel plant corresponding to the preset cash price and meeting a preset condition as a candidate steel plant; determining an expected processing efficiency of the scrap steel to be processed based on a processing size requirement, the scrap steel type and the scrap steel basic information; determining a target steel plant based on the expected processing efficiency and the candidate steel plant, and taking the processing size requirement as a target processing size; generating an intelligent decision-making report based on the target steel plant, the target processing size, the expected cash price and the expected processing efficiency; and controlling a scrap steel processing device to process the scrap steel to be processed according to the target processing size. The application has the effect of improving the comprehensive efficiency and economic benefits of scrap steel processing.

Description

Technical Field

[0001] This application relates to the technical field of scrap steel processing, and in particular to an intelligent decision-making method, apparatus, equipment and medium for scrap steel processing. Background Technology

[0002] In the current scrap steel recycling and processing industry, processing equipment typically relies on manual experience to set the size parameters for crushing, shearing, or briquetting, and the differentiation of different types of scrap steel is still mainly based on visual inspection and manual sorting. However, downstream steel companies have significantly different purchasing standards for the same type of scrap steel, especially regarding processing size requirements, and the prices paid by each steel mill fluctuate in real time depending on factors such as specifications and cleanliness. In practice, to avoid scrap steel being rejected due to exceeding size limits, operators often process the scrap steel to be too small to meet the minimum size threshold of most steel mills. While this practice reduces the risk of being returned, it keeps the overall processing efficiency at a low level for a long time.

[0003] Meanwhile, there is a lack of effective automatic linkage between scrap steel processing equipment and dynamic market price information. Operators struggle to obtain timely updates on the latest quotations for specific scrap steel types from different steel mills nationwide, along with their corresponding size and acceptance requirements. Furthermore, they cannot quickly compare the prices received from various steel mills before processing. Scrap steel type identification remains highly reliant on manual labor, which is not only inefficient and prone to misjudgments but also prevents the dynamic adjustment of processing parameters based on the actual scrap steel type and optimal economic objectives. Therefore, existing technologies suffer from a disconnect between scrap steel processing size decisions and market demand, low levels of automation in type identification, and a pressing need to improve processing efficiency and economics. How to improve the intelligent decision-making capability for processing sizes based on different scrap steel types and fluctuating market conditions during scrap steel processing, thereby enhancing the overall efficiency and economic benefits of scrap steel treatment, has become a critical technical problem that needs to be solved in this field. Summary of the Invention

[0004] In order to improve the overall efficiency and economic benefits of scrap steel processing, this application provides an intelligent decision-making method, device, equipment and medium for scrap steel processing.

[0005] Firstly, this application provides an intelligent decision-making method for scrap steel processing, employing the following technical solution: A smart decision-making method for scrap steel processing includes: Obtain the scrap steel type, basic scrap steel information, and real-time quotations from all steel mills within a preset range that purchase the scrap steel type to be processed; Based on the real-time price quote, the basic information on scrap steel, and the preset rules for calculating the price to be received, the estimated price to be received for the scrap steel to be processed is calculated. Select steel mills that meet the preset conditions from the preset take-home prices as candidate steel mills; Obtain the processing size requirements of the candidate steel mills; Based on the processing size requirements, the scrap steel type, and the basic information of the scrap steel, the expected processing efficiency of the scrap steel to be processed is determined; The target steel mill is determined based on the expected processing efficiency and the candidate steel mills, and the processing size requirements of the target steel mill are used as the target processing size. A smart decision-making report is generated based on the target steel mill, the target processing size, the expected final price, and the expected processing efficiency. The scrap steel processing equipment is controlled to process the scrap steel to be processed according to the target processing size.

[0006] By adopting the above technical solution, the type and basic information of the scrap steel to be processed, as well as real-time quotations from various steel mills nationwide that purchase this type of scrap steel, are obtained. Based on preset price calculation rules, the expected price to each steel mill is quantitatively calculated. This eliminates the reliance on manual judgment of lagging information during scrap steel processing, significantly improving the intelligence of price decision-making. Furthermore, by combining the scrap steel type and basic information to determine the expected processing efficiency, the processing dimensions are precisely matched to the actual purchasing standards of specific steel mills. This avoids the waste of time, energy, and tool wear caused by blindly processing too finely to avoid returns, thus effectively improving the operational efficiency of scrap steel processing. Simultaneously, this method comprehensively considers the expected processing efficiency and the candidate steel mills to determine the target steel mill and target dimensions, achieving a reasonable balance between the price and processing efficiency, avoiding the one-sided pursuit of high prices while ignoring processing costs. The decision results are directly linked to the control of processing equipment, reducing human error and operational deviations. Because the processed scrap steel's dimensions precisely match the steel mill's acceptance standards, the delivery qualification rate is improved, avoiding returns or secondary processing losses due to size discrepancies, comprehensively improving the overall efficiency and economic benefits of scrap steel processing.

[0007] Optionally, the calculation of the expected net price of the scrap steel to be processed based on the real-time quote, the basic information of the scrap steel, and the preset net price calculation rules includes: Extract the steel mill congestion status, estimated weight, estimated impurity content, current location, purchase unit price in the real-time quotation, and the location of all steel mills from the basic information of the scrap steel. Based on the estimated weight and the estimated impurity content, the net weight after deduction is calculated according to the preset deduction rules; Based on the net weight and the purchase unit price, calculate the basic net amount received by each steel mill; A preset transportation cost database is used to calculate the estimated transportation cost for each steel mill based on the current location, the steel mill location, and the preset transportation cost database. The estimated transportation cost and the steel mill's truck congestion situation are subtracted from the basic amount received to generate the expected price received by each steel mill.

[0008] Optionally, determining the expected processing efficiency of the scrap steel to be processed based on the processing size requirements, the scrap steel type, and the basic information of the scrap steel includes: Extract the original dimensions from the basic information of the scrap steel, and the upper and lower limits of the target dimensions from the processing dimension requirements; Based on the original size, the upper limit of the target size, and the lower limit of the target size, calculate the excess volume in the scrap steel to be processed that needs to be adjusted; Obtain historical processing records that match the scrap steel type; Based on the historical processing records, the excess volume, and the preset processing efficiency prediction model, the expected processing efficiency is calculated.

[0009] Optionally, selecting steel mills that meet the preset conditions from the preset take-home prices as candidate steel mills includes: Based on the preset conditions, determine the price filtering threshold and the distance filtering threshold; The expected net price of each steel mill is compared with the price screening threshold to generate a price comparison result; Based on the price comparison results, a first set of candidate steel mills is selected from all steel mills whose expected delivery price is greater than or equal to the price screening threshold. Based on the steel plant locations of each candidate steel plant in the first candidate steel plant set and the current location, calculate the transportation distance between each candidate steel plant and the scrap steel to be processed. Each of the aforementioned transportation distances is compared with the distance filtering threshold to generate a distance comparison result; Based on the distance comparison results, a second set of candidate steel mills with a transportation distance not greater than the distance screening threshold is selected from the first set of candidate steel mills. If the second candidate steel mill set is not empty, then the steel mill with the highest expected price is selected from the second candidate steel mill set as the alternative steel mill; If the second candidate steel mill set is empty, then the steel mill with the shortest transportation distance is selected from the first candidate steel mill set as the alternative steel mill.

[0010] Optionally, determining the target steel mill based on the expected processing efficiency and the candidate steel mills includes: Determine whether the number of candidate steel mills is unique; If the number of candidate steel mills is unique, then the candidate steel mill will be used as the target steel mill. If the number of candidate steel mills is not unique, then a preset efficiency threshold is obtained; Compare the expected processing efficiency of the candidate steel mills with the efficiency threshold to determine whether there are any steel mills whose expected processing efficiency is not lower than the efficiency threshold. If there is a steel mill with an expected processing efficiency not lower than the efficiency threshold, then the steel mill with the highest expected processing efficiency is identified as the target steel mill. If there is no steel mill with an expected processing efficiency not lower than the efficiency threshold, then the steel mill with the highest expected final price is selected as the target steel mill.

[0011] Optionally, after generating the intelligent decision report, the method further includes: The intelligent decision report is sent to the user terminal, and feedback instructions are received from the user terminal. The feedback instruction is type-determined, and a determination result is generated; If the judgment result is a confirmation instruction, then the step of controlling the scrap steel processing equipment to process according to the target processing size is executed; If the determination result is a modification instruction, then extract the user-specified modified data from the modification instruction; If the modified data refers to the modified steel mill, then the modified steel mill is taken as the target steel mill, and the processing size requirements corresponding to the modified steel mill are obtained as the target processing size. If the modified data is the modified processing dimension, then the modified processing dimension shall be used as the target processing dimension, and the original target steel mill shall remain unchanged; The modified data is used to control the scrap steel processing equipment for processing.

[0012] Optionally, the method further includes: After the scrap steel processing equipment completes processing according to the target processing size, the actual processing time and the actual amount received are obtained. Calculate the actual processing efficiency based on the actual processing time. The actual processing efficiency is compared with the expected processing efficiency to generate an efficiency deviation. The actual amount received is compared with the expected price received to generate a price deviation. Based on the efficiency deviation, the preset processing efficiency prediction model is updated according to the preset efficiency feedback correction rule to generate an updated prediction model; Based on the price deviation, the preset final price calculation rule is adjusted to generate an adjusted price calculation rule; The updated prediction model and the adjusted price calculation rules are added to a preset correction database for use in the next scrap steel processing calculation.

[0013] Secondly, this application provides an intelligent decision-making device for scrap steel processing, which adopts the following technical solution: An intelligent decision-making device for scrap steel processing includes: The scrap steel information acquisition module is used to acquire the scrap steel type, basic scrap steel information, and real-time quotations from all steel mills within a preset range that purchase the scrap steel type to be processed. The final price calculation module is used to calculate the expected final price of the scrap steel to be processed based on the real-time price, the basic information of the scrap steel, and the preset final price calculation rules. The alternative steel mill selection module is used to select steel mills that meet the preset conditions from the preset take-home prices as alternative steel mills; The processing requirements acquisition module is used to acquire the processing size requirements of the candidate steel mills; The processing efficiency confirmation module is used to determine the expected processing efficiency of the scrap steel to be processed based on the processing size requirements, the scrap steel type, and the basic information of the scrap steel. The target steel mill determination module is used to determine the target steel mill based on the expected processing efficiency and the candidate steel mills, and to use the processing size requirements of the target steel mill as the target processing size; The decision report generation module is used to generate an intelligent decision report based on the target steel mill, the target processing size, the expected final price, and the expected processing efficiency. The scrap steel processing control module is used to control the scrap steel processing equipment to process the scrap steel to be processed according to the target processing size.

[0014] By adopting the above technical solution, the type and basic information of the scrap steel to be processed, as well as real-time quotations from various steel mills nationwide that purchase this type of scrap steel, are obtained. Based on preset price calculation rules, the expected price to each steel mill is quantitatively calculated. This eliminates the reliance on manual judgment of lagging information during scrap steel processing, significantly improving the intelligence of price decision-making. Furthermore, by combining the scrap steel type and basic information to determine the expected processing efficiency, the processing dimensions are precisely matched to the actual purchasing standards of specific steel mills. This avoids the waste of time, energy, and tool wear caused by blindly processing too finely to avoid returns, thus effectively improving the operational efficiency of scrap steel processing. Simultaneously, this method comprehensively considers the expected processing efficiency and the candidate steel mills to determine the target steel mill and target dimensions, achieving a reasonable balance between the price and processing efficiency, avoiding the one-sided pursuit of high prices while ignoring processing costs. The decision results are directly linked to the control of processing equipment, reducing human error and operational deviations. Because the processed scrap steel's dimensions precisely match the steel mill's acceptance standards, the delivery qualification rate is improved, avoiding returns or secondary processing losses due to size discrepancies, comprehensively improving the overall efficiency and economic benefits of scrap steel processing.

[0015] Thirdly, this application provides an electronic device that adopts the following technical solution: An electronic device includes a processor coupled to a memory; The processor is configured to execute a computer program stored in the memory, such that the electronic device executes the computer program of the intelligent decision-making method for scrap steel processing as described in any of the first aspects.

[0016] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium storing a computer program capable of being loaded by a processor and executing the intelligent decision-making method for scrap steel processing as described in any of the first aspects.

[0017] In summary, this application includes at least one of the following beneficial technical effects: This method obtains the type and basic information of the scrap steel to be processed, as well as real-time quotations from steel mills nationwide that purchase this type of scrap steel. Based on pre-defined rules for calculating the expected price to each steel mill, it eliminates reliance on manual judgment of lagging information during scrap steel processing, significantly improving the intelligence of price decisions. Furthermore, by combining the scrap steel type and basic information to determine the expected processing efficiency, the method ensures that the processing dimensions are precisely matched to the actual purchasing standards of specific steel mills. This avoids the waste of time, energy, and tool wear caused by blindly processing too finely to avoid returns, thus effectively improving the operational efficiency of scrap steel processing. Simultaneously, this method comprehensively considers the expected processing efficiency and the candidate steel mills to determine the target steel mill and target dimensions, achieving a reasonable balance between the price and processing efficiency, avoiding the one-sided pursuit of high prices while ignoring processing costs. The decision results are directly linked to the control of processing equipment, reducing human error and operational deviations. Because the processed scrap steel's dimensions precisely match the steel mill's acceptance standards, the delivery qualification rate is improved, avoiding returns or secondary processing losses due to size discrepancies, comprehensively improving the overall efficiency and economic benefits of scrap steel processing. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating an intelligent decision-making method for scrap steel processing provided in an embodiment of this application.

[0019] Figure 2 This is a structural block diagram of an intelligent decision-making device for scrap steel processing provided in an embodiment of this application.

[0020] Figure 3 This is a structural block diagram of the electronic device provided in the embodiments of this application. Detailed Implementation

[0021] The present application will be further described in detail below with reference to the accompanying drawings.

[0022] This application provides an intelligent decision-making method for scrap steel processing. This method can be executed by an electronic device, which can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, desktop computer, etc., but is not limited to these.

[0023] Figure 1 This is a flowchart illustrating an intelligent decision-making method for scrap steel processing provided in an embodiment of this application.

[0024] like Figure 1 As shown, the main process of this method is described below (steps S101 to S108): Step S101: Obtain the scrap steel type, basic scrap steel information, and real-time quotations from all steel mills within the preset range that purchase scrap steel of the desired type.

[0025] In this embodiment, upon receiving the scrap steel to be processed, it is scanned and weighed to obtain the scrap steel type and estimated weight. The scrap steel type refers to the material classification of the scrap steel, such as carbon steel, alloy steel, stainless steel, etc., or it can be classified according to its source and form, such as heavy scrap, medium scrap, light scrap, and offcuts. Basic scrap steel information includes the estimated weight obtained from weighing, estimated impurity content, current storage location, and original dimensions. Real-time quotes refer to the current purchase prices offered by various steel mills within a preset range for a specific type of scrap steel. These quotes typically include the unit purchase price and may dynamically change with market supply and demand, scrap steel quality, and other factors.

[0026] Step S102: Calculate the expected price of the scrap steel to be processed based on real-time quotes, basic scrap steel information, and preset price calculation rules.

[0027] For step S102, extract the steel mill congestion status, estimated weight, estimated impurity content, current location, purchase price in real-time quotations, and the location of all steel mills from the basic scrap steel information; calculate the net weight after deduction based on the estimated weight and estimated impurity content according to the preset deduction rules; calculate the basic amount received from each steel mill based on the net weight and purchase price; preset a transportation cost database, and calculate the estimated transportation cost for each steel mill based on the current location, steel mill location, and preset transportation cost database; subtract the estimated transportation cost and steel mill congestion status from the basic amount received to generate the expected price received from each steel mill.

[0028] In this embodiment, actual scrap steel transactions often require deducting impurities. Pre-set deduction rules are established based on industry standards, steel mill requirements, or experience, and are used to convert the estimated impurity content into a weight deduction. The basic net proceeds refer to the theoretical revenue that can be obtained from selling scrap steel to a specific steel mill, without considering transportation costs and other expenses. It is calculated by multiplying the net weight after deduction by the purchase price offered by the steel mill. Since the purchase prices of different steel mills may vary, calculations need to be performed separately for each steel mill for subsequent comparison. Transportation costs are a crucial factor affecting the actual net proceeds price of scrap steel. A pre-set transportation cost database stores unit transportation cost information for different distances, transportation methods, and scrap steel types. By querying this database and combining the current location of the scrap steel with the geographical locations of various steel mills, the estimated transportation distance required to transport the scrap steel to each steel mill can be calculated. Then, based on a pre-set transportation cost model, the corresponding transportation cost can be estimated, making the calculation of transportation costs more automated and accurate. Steel mill congestion is another important factor affecting the actual price of scrap steel received. After scrap steel is delivered to the corresponding steel mill, it needs to wait for unloading. However, if a steel mill offers a high price, a large amount of scrap steel will be transported to that mill. Consequently, upon arrival, the scrap steel cannot be unloaded within the scheduled time and must queue up. During this waiting period, vehicles carrying scrap steel cannot engage in other transportation activities, resulting in vehicle damage. Therefore, the cost of overdue congestion is calculated based on actual congestion information and considered an important component of steel mill congestion. The expected price received is the actual net profit after deducting transportation costs and other necessary expenses from the scrap steel sold to a specific steel mill. By subtracting the corresponding estimated transportation costs and the cost of overdue congestion from the base price received by each steel mill, the final expected price received by that steel mill can be obtained. This calculation result directly reflects the actual economic benefits of scrap steel at different steel mills, providing a quantitative basis for subsequent intelligent decision-making.

[0029] Step S103: Select steel mills that meet the preset conditions from the preset prices as candidate steel mills.

[0030] For step S103, a price screening threshold and a distance screening threshold are determined based on preset conditions; the expected delivery price of each steel mill is compared with the price screening threshold to generate a price comparison result; based on the price comparison result, a first candidate steel mill set with an expected delivery price greater than or equal to the price screening threshold is selected from all steel mills; based on the current position of the steel mills in the first candidate steel mill set, the transportation distance between each candidate steel mill and the scrap steel to be processed is calculated; each transportation distance is compared with the distance screening threshold to generate a distance comparison result; based on the distance comparison result, a second candidate steel mill set with a transportation distance not greater than the distance screening threshold is selected from the first candidate steel mill set; if the second candidate steel mill set is not empty, the steel mill with the highest expected delivery price is selected from the second candidate steel mill set as the candidate steel mill; if the second candidate steel mill set is empty, the steel mill with the smallest transportation distance is selected from the first candidate steel mill set as the candidate steel mill.

[0031] In this embodiment, when determining candidate steel mills, it is first necessary to determine price screening thresholds and distance screening thresholds based on preset conditions. These preset conditions are screening criteria set by the user or system according to actual needs and experience. To refine the steel mill screening, the preset conditions are specified into two key indicators: price screening thresholds and distance screening thresholds. The price screening threshold is used to set the minimum acceptable standard for the expected delivery price, ensuring that the selected steel mills have a certain economic attractiveness. The distance screening threshold is used to limit the maximum acceptable transportation distance between the steel mill and the current location of the scrap steel, in order to control transportation costs and logistics efficiency. Subsequently, all steel mills that have obtained real-time quotations are traversed, and their respective expected delivery prices are compared one by one with the preset price screening thresholds. Based on the price comparison results, all steel mills whose expected delivery prices reach or exceed the price screening thresholds are gathered to form a first candidate steel mill set. This set includes all potential cooperative steel mills that meet the initial requirements in terms of price.

[0032] After identifying steel mills with attractive prices, logistical factors need to be considered. Based on the geographical location of each mill in the first candidate set and the current location of the scrap steel to be processed, the actual transportation distance between them is precisely calculated. The calculated transportation distances for each candidate mill are compared with a preset distance screening threshold, eliminating mills that, while having suitable prices, have excessively long transportation distances, resulting in high transportation costs or logistical inconveniences. Combining the distance comparison results, a second candidate set is formed by further filtering from the first candidate set to include mills whose transportation distances also meet the distance screening threshold. The mills in this second set are not only competitive in price but also geographically reasonable, making them superior potential partners.

[0033] If, after two rounds of screening, the second candidate steel mill set still contains steel mills that meet the criteria, it indicates that there are multiple high-quality options that meet both price and distance requirements. The steel mill with the highest expected delivery price will be selected as the final alternative. If, after dual screening based on price and distance, the second candidate steel mill set is empty (i.e., no steel mill simultaneously meets the price and distance thresholds), then, to ensure that at least one feasible alternative is found, we will revert to the first candidate steel mill set and select the steel mill with the shortest transportation distance as the alternative.

[0034] Step S104: Obtain the processing size requirements of the candidate steel mills.

[0035] In this embodiment, the processing size requirements refer to the specific regulations of each steel mill on the physical dimensions of the scrap steel it purchases, such as length, width, and thickness. The scrap steel must meet these size requirements to be accepted by the steel mill. Therefore, after the candidate steel mills are determined, their corresponding processing size requirements are collected.

[0036] Step S105: Based on the processing size requirements, scrap steel type, and basic scrap steel information, determine the expected processing efficiency of the scrap steel to be processed.

[0037] For step S105, extract the original size from the basic information of scrap steel, and the upper and lower limits of the target size in the processing size requirements; based on the original size and the upper and lower limits of the target size, calculate the excess volume that needs to be adjusted in the scrap steel to be processed; obtain historical processing records that match the type of scrap steel; and calculate the expected processing efficiency based on the historical processing records, the excess volume, and the preset processing efficiency prediction model.

[0038] In this embodiment, the original dimensions refer to the actual physical dimensions of the scrap steel before any processing. These dimensions are the basic data for assessing the difficulty of scrap steel processing. Target size upper and lower limits are also extracted from the processing size requirements of candidate steel mills. The target dimensions limit the size range of the processed scrap steel for the steel mills. These size requirements are the final goals of scrap steel processing and directly determine the degree to which the scrap steel needs to be cut, compacted, or crushed. Excess volume refers to the volume occupied by the portion of the scrap steel's original dimensions that exceeds the steel mill's target size requirements. For example, if a certain dimension of the scrap steel exceeds the target size upper limit or is smaller than the target size lower limit, the volume corresponding to this excess or deficiency dimension is the excess volume. Calculating the excess volume is a key step in quantifying the workload of scrap steel processing. By comparing various dimensions of the scrap steel with the target size range, the parts that need to be cut, compacted, or shaped can be identified, and their volume estimated.

[0039] Historical processing records contain detailed data on past processing of the same or similar scrap steel types. This historical data is crucial for establishing and optimizing processing efficiency prediction models. Ultimately, the system calculates the expected processing efficiency based on historical processing records, excess volume, and the preset processing efficiency prediction model. The preset processing efficiency prediction model is a mathematical model used to predict processing efficiency based on input parameters. By learning the relationship between excess volume and actual processing efficiency in historical data, the model can output a predicted processing efficiency value based on the excess volume and type of scrap steel to be processed, combined with empirical parameters from historical processing records.

[0040] Step S106: Based on the expected processing efficiency and alternative steel mills, determine the target steel mill and use the processing size requirements of the target steel mill as the target processing size.

[0041] For step S106, determine whether the number of candidate steel mills is unique; if the number of candidate steel mills is unique, then the candidate steel mill is selected as the target steel mill; if the number of candidate steel mills is not unique, then obtain a preset efficiency threshold; compare the expected processing efficiency corresponding to the candidate steel mill with the efficiency threshold to determine whether there is a steel mill whose expected processing efficiency is not lower than the efficiency threshold; if there is a steel mill whose expected processing efficiency is not lower than the efficiency threshold, then the steel mill with the highest expected processing efficiency is selected as the target steel mill; if there is no steel mill whose expected processing efficiency is not lower than the efficiency threshold, then the steel mill with the highest expected final price is selected as the target steel mill.

[0042] In this embodiment, the initial set of candidate steel mills needs to be evaluated for quantity. If the set contains only one steel mill, that single candidate mill is directly identified as the target mill, eliminating the need for further complex decisions and simplifying the decision-making process. When the number of candidate steel mills is not unique, a preset efficiency threshold is introduced to balance economic benefits and processing efficiency. The preset efficiency threshold is a pre-set benchmark value used to measure scrap steel processing efficiency, which can be set based on factors such as industry standards, historical processing data, equipment performance, or user preferences. After obtaining the preset efficiency threshold, the expected processing efficiency corresponding to each candidate steel mill is compared with the preset efficiency threshold.

[0043] Based on the comparison results, the system further determines whether there are steel mills with expected processing efficiency not lower than the efficiency threshold. If at least one such steel mill exists, it indicates that there are still multiple options while meeting the basic efficiency requirements. In this case, to maximize overall benefits, the system will further select the steel mill with the highest expected processing efficiency from among those that meet the efficiency threshold as the final target steel mill. Conversely, if, after comparison, it is found that the expected processing efficiency of all candidate steel mills fails to reach the preset efficiency threshold, it means that among the current options, no steel mill can be found that meets both the price condition and the minimum efficiency requirement. In this situation, to avoid decision stagnation or choosing a suboptimal solution, the system will adjust its strategy and select the steel mill with the highest expected final price as the target steel mill.

[0044] Step S107: Generate an intelligent decision report based on the target steel mill, target processing size, expected price, and expected processing efficiency.

[0045] In this embodiment, the target steel mill, target processing size, expected price, and expected processing efficiency are integrated to obtain an intelligent decision report that can acquire all of the above information.

[0046] In this embodiment, an intelligent decision report is sent to the user terminal, and feedback instructions returned by the user terminal are received. The type of the feedback instructions is determined, and a determination result is generated. If the determination result is a confirmation instruction, the step of controlling the scrap steel processing equipment to process according to the target processing size is executed. If the determination result is a modification instruction, the modified data specified by the user is extracted from the modification instruction. If the modified data is a modified steel mill, the modified steel mill is used as the target steel mill, and the processing size requirement corresponding to the modified steel mill is obtained as the target processing size. If the modified data is a modified processing size, the modified processing size is used as the target processing size, and the original target steel mill remains unchanged. The scrap steel processing equipment is controlled to perform processing based on the modified data.

[0047] The intelligent decision-making report contains key decision-making information derived by the system based on preset rules and data analysis, including the target steel mill, target processing dimensions, expected final price, and expected processing efficiency. Sending this report to the user terminal and receiving feedback instructions signifies that the system is no longer a one-way decision output, but has established a two-way interactive channel, allowing users to confirm the system's suggested decisions or propose modifications. Upon receiving user feedback, the nature of the feedback instruction needs to be identified. Typically, feedback instructions can be categorized as confirmation or modification instructions. The determination result will clearly indicate whether the user accepts the current decision suggestion or wishes to adjust the decision.

[0048] When a user explicitly accepts the intelligent decision report generated by the system (i.e., the judgment result is a confirmation instruction), the system will directly start the scrap steel processing equipment to perform the actual processing operation according to the target steel mill and target processing size specified in the report. When a user is not entirely satisfied with the system's suggestions and chooses to modify them, the system needs to accurately identify the specific content the user wishes to adjust from the modification instructions provided. These instructions may include changes to the target steel mill or adjustments to the target processing size. If the user chooses to change the target steel mill, the modified steel mill specified by the user will be set as the new target steel mill. Since different steel mills may have specific requirements for the processing size of scrap steel, the system will re-query or obtain the corresponding processing size requirements based on this new target steel mill and set it as the new target processing size to ensure that the processed scrap steel meets the new target steel mill's acceptance standards and avoid transaction problems caused by size discrepancies. If the user only wishes to adjust the processing size, the modified processing size specified by the user will be set as the new target processing size.

[0049] Whether the user confirms the original decision or a new decision is formed after modification, the scrap steel processing equipment will be controlled to carry out actual processing operations based on the latest decision result that has been confirmed or modified by the user.

[0050] Step S108: Control the scrap steel processing equipment to process the scrap steel to be processed according to the target processing size.

[0051] In this embodiment, after the final processing size is determined, the scrap steel processing equipment will process the scrap steel according to the target processing size and sell the processed scrap steel in a timely manner.

[0052] In this embodiment, after the scrap steel processing equipment completes processing according to the target processing size, the actual processing time and actual net proceeds are obtained; the actual processing efficiency is calculated based on the actual processing time; the actual processing efficiency is compared with the expected processing efficiency to generate an efficiency deviation; the actual net proceeds are compared with the expected net proceeds price to generate a price deviation; based on the efficiency deviation, the preset processing efficiency prediction model is updated according to the preset efficiency feedback correction rule to generate an updated prediction model; based on the price deviation, the preset net proceeds price calculation rule is adjusted to generate an adjusted price calculation rule; the updated prediction model and the adjusted price calculation rule are added to the preset correction database for use in the next scrap steel processing calculation.

[0053] After the scrap steel processing equipment completes the processing of the scrap steel according to the target size, the actual processing time and the actual amount received will be obtained. The actual processing time refers to the actual time spent by the scrap steel processing equipment to complete the processing of a specific piece of scrap steel, and the actual amount received refers to the final amount received after the scrap steel is sold to the target steel mill and all actual costs incurred are deducted.

[0054] Actual processing efficiency is an indicator that measures the actual performance of the scrap steel processing process, and its calculation method can be set according to specific business needs. Efficiency deviation quantifies the difference between actual and predicted processing performance, and can be calculated in various ways, such as direct subtraction, calculating percentage difference, or setting an error range. Furthermore, the actual amount received is compared with the estimated price received during the decision-making stage to generate price deviation. Price deviation reflects the difference between actual and predicted revenue, and can also be calculated in this way, such as direct subtraction, calculating percentage difference, or setting an error range.

[0055] Predefined efficiency feedback correction rules are a set of predefined algorithms or strategies used to guide how to adjust the parameters, weights, or structure of a prediction model based on efficiency deviations. For example, if actual efficiency consistently exceeds expected efficiency, the correction rule might instruct model parameters to be adjusted upwards; if actual efficiency consistently falls below expected efficiency, it would be adjusted downwards. Similarly, based on price deviations, the system adjusts the predefined take-home price calculation rules, thereby generating adjusted price calculation rules. These predefined take-home price calculation rules might include parameters such as impurity deduction rates, transportation cost coefficients, and fluctuation factors in purchase unit prices. Price deviations can serve as the basis for adjusting these parameters. The preset correction database is a dedicated storage unit for storing the prediction models and calculation rules that have been corrected through feedback. Storing these optimized models and rules allows the system to call upon them when making scrap steel processing decisions in the future, thereby improving the accuracy and reliability of the decisions.

[0056] Figure 2 The present invention relates to a structural block diagram of an intelligent decision-making device 200 for scrap steel processing provided in the embodiments of the application.

[0057] like Figure 2 As shown, the intelligent decision-making device 200 for scrap steel processing mainly includes: The scrap steel information acquisition module 201 is used to acquire the scrap steel type, basic scrap steel information, and real-time quotations from all steel mills that purchase scrap steel of the preset range within the range of the scrap steel to be processed. The first-hand price calculation module 202 is used to calculate the expected first-hand price of the scrap steel to be processed based on real-time quotations, basic scrap steel information and preset first-hand price calculation rules. The alternative steel mill selection module 203 is used to select steel mills that meet the preset conditions from the preset prices as alternative steel mills; The processing requirements acquisition module 204 is used to acquire the processing size requirements of the candidate steel mills; The processing efficiency confirmation module 205 is used to determine the expected processing efficiency of the scrap steel to be processed based on the processing size requirements, scrap steel type and basic scrap steel information; The target steel mill determination module 206 is used to determine the target steel mill based on the expected processing efficiency and alternative steel mills, and to use the processing size requirements of the target steel mill as the target processing size; The decision report generation module 207 is used to generate intelligent decision reports based on the target steel mill, target processing size, expected price, and expected processing efficiency. The scrap steel processing control module 208 is used to control the scrap steel processing equipment to process the scrap steel to be processed according to the target processing size.

[0058] As an optional implementation of this embodiment, the final price calculation module 202 is specifically used to extract the steel mill congestion situation, estimated weight, estimated impurity content, current location, purchase price in real-time quotations, and the location of all steel mills from the basic information of scrap steel; calculate the net weight after deduction based on the estimated weight and estimated impurity content according to the preset deduction rules; calculate the basic final amount received from each steel mill based on the net weight and purchase price; preset a transportation cost database, and calculate the estimated transportation cost of each steel mill based on the current location, steel mill location, and preset transportation cost database; subtract the estimated transportation cost and steel mill congestion situation from the basic final amount received to generate the expected final price received from each steel mill.

[0059] As an optional implementation of this embodiment, the processing efficiency confirmation module 205 is specifically used to extract the original size from the basic information of scrap steel, the upper limit of the target size in the processing size requirements, and the lower limit of the target size; calculate the excess volume that needs to be adjusted in the scrap steel to be processed based on the original size, the upper limit of the target size, and the lower limit of the target size; obtain historical processing records that match the type of scrap steel; and calculate the expected processing efficiency based on the historical processing records, the excess volume, and the preset processing efficiency prediction model.

[0060] As an optional implementation of this embodiment, the candidate steel mill selection module 203 is specifically used to determine a price screening threshold and a distance screening threshold based on preset conditions; compare the expected delivery price of each steel mill with the price screening threshold to generate a price comparison result; based on the price comparison result, select a first candidate steel mill set from all steel mills whose expected delivery price is greater than or equal to the price screening threshold; calculate the transportation distance between each candidate steel mill and the scrap steel to be processed based on the steel mill location and current location of each candidate steel mill in the first candidate steel mill set; compare each transportation distance with the distance screening threshold to generate a distance comparison result; based on the distance comparison result, select a second candidate steel mill set from the first candidate steel mill set whose transportation distance is not greater than the distance screening threshold; if the second candidate steel mill set is not empty, select the steel mill with the highest expected delivery price from the second candidate steel mill set as the candidate steel mill; if the second candidate steel mill set is empty, select the steel mill with the smallest transportation distance from the first candidate steel mill set as the candidate steel mill.

[0061] As an optional implementation of this embodiment, the target steel mill determination module 206 is specifically used to determine whether the number of candidate steel mills is unique; if the number of candidate steel mills is unique, then the candidate steel mill is used as the target steel mill; if the number of candidate steel mills is not unique, then a preset efficiency threshold is obtained; the expected processing efficiency corresponding to the candidate steel mill is compared with the efficiency threshold to determine whether there is a steel mill whose expected processing efficiency is not lower than the efficiency threshold; if there is a steel mill whose expected processing efficiency is not lower than the efficiency threshold, then the steel mill with the highest expected processing efficiency is determined as the target steel mill; if there is no steel mill whose expected processing efficiency is not lower than the efficiency threshold, then the steel mill with the highest expected final price is selected as the target steel mill.

[0062] As an optional implementation of this embodiment, the intelligent decision-making device 200 for scrap steel processing further includes: The feedback instruction receiving module is used to send the intelligent decision report to the user terminal and receive the feedback instructions returned by the user terminal. The judgment result generation module is used to determine the type of feedback instructions and generate judgment results. The scrap steel processing execution module is used to execute and control the scrap steel processing equipment to process scrap steel according to the target processing size. The modified data extraction module is used to extract the modified data specified by the user from the modification command; The first data modification module is used to take the modified steel mill as the target steel mill and obtain the corresponding processing size requirements of the modified steel mill as the target processing size. The second data modification module is used to take the modified processing dimensions as the target processing dimensions and keep the original target steel mill unchanged. Modify the data processing module to control scrap steel processing equipment for processing based on the modified data.

[0063] As an optional implementation of this embodiment, the intelligent decision-making device 200 for scrap steel processing further includes: The actual data acquisition module is used to obtain the actual processing time and actual amount received after the scrap steel processing equipment has completed processing according to the target processing size. The actual efficiency calculation module is used to calculate the actual processing efficiency based on the actual processing time. The efficiency deviation generation module is used to compare the actual processing efficiency with the expected processing efficiency and generate an efficiency deviation. The price deviation generation module is used to compare the actual amount received with the expected amount received and generate a price deviation. The prediction model update module is used to update the preset processing efficiency prediction model based on the efficiency deviation and according to the preset efficiency feedback correction rules, and generate an updated prediction model. The calculation rule adjustment module is used to adjust the preset price calculation rules based on price deviations and generate adjusted price calculation rules. The data storage module is updated to update the prediction model and adjust the price calculation rules to the preset correction database for use in the next scrap steel processing calculation.

[0064] In one example, the module in any of the above devices may be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0065] For example, when modules in a device can be implemented via a processing element scheduler, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor capable of calling programs. Alternatively, these modules can be integrated together as a system-on-a-chip (SOC).

[0066] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described device and module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0067] Figure 3 This is a structural block diagram of the electronic device 300 provided in an embodiment of this application.

[0068] like Figure 3 As shown, the electronic device 300 includes a processor 301 and a memory 302, and may further include one or more of an information input / output (I / O) interface 303, a communication component 304, and a communication bus 305.

[0069] The processor 301 controls the overall operation of the electronic device 300 to complete all or part of the steps of the intelligent decision-making method for scrap steel processing described above. The memory 302 stores various types of information to support the operation of the electronic device 300, which may include, for example, instructions for any application or method operating on the electronic device 300, as well as application-related information. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as one or more of Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0070] I / O interface 303 provides an interface between processor 301 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 304 is used for wired or wireless communication between electronic device 300 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 304 may include a Wi-Fi component, a Bluetooth component, and an NFC component.

[0071] The electronic device 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the intelligent decision-making method for scrap steel processing given in the above embodiments.

[0072] The communication bus 305 may include a path for transmitting information between the aforementioned components. The communication bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 305 can be divided into address bus, bus, control bus, etc.

[0073] Electronic device 300 may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers, and may also be servers.

[0074] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the intelligent decision-making method for scrap steel processing described above.

[0075] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0076] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0077] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. An intelligent decision-making method for scrap steel processing, characterized in that, include: Obtain the scrap steel type, basic scrap steel information, and real-time quotations from all steel mills within a preset range that purchase the scrap steel type to be processed; Based on the real-time price quote, the basic information on scrap steel, and the preset rules for calculating the price to be received, the estimated price to be received for the scrap steel to be processed is calculated. Select steel mills that meet the preset conditions from the preset take-home prices as candidate steel mills; Obtain the processing size requirements of the candidate steel mills; Based on the processing size requirements, the scrap steel type, and the basic information of the scrap steel, the expected processing efficiency of the scrap steel to be processed is determined; The target steel mill is determined based on the expected processing efficiency and the candidate steel mills, and the processing size requirements of the target steel mill are used as the target processing size. A smart decision-making report is generated based on the target steel mill, the target processing size, the expected final price, and the expected processing efficiency. The scrap steel processing equipment is controlled to process the scrap steel to be processed according to the target processing size.

2. The method according to claim 1, characterized in that, The calculation of the expected net price of the scrap steel to be processed, based on the real-time price quote, the basic information of the scrap steel, and the preset net price calculation rules, includes: Extract the steel mill congestion status, estimated weight, estimated impurity content, current location, purchase unit price in the real-time quotation, and the location of all steel mills from the basic information of the scrap steel. Based on the estimated weight and the estimated impurity content, the net weight after deduction is calculated according to the preset deduction rules; Based on the net weight and the purchase unit price, calculate the basic net amount received by each steel mill; A preset transportation cost database is used to calculate the estimated transportation cost for each steel mill based on the current location, the steel mill location, and the preset transportation cost database. The estimated transportation cost and the steel mill's truck congestion situation are subtracted from the basic amount received to generate the expected price received by each steel mill.

3. The method according to claim 1, characterized in that, The process of determining the expected processing efficiency of the scrap steel to be processed based on the processing size requirements, the scrap steel type, and the basic information of the scrap steel includes: Extract the original dimensions from the basic information of the scrap steel, and the upper and lower limits of the target dimensions from the processing dimension requirements; Based on the original size, the upper limit of the target size, and the lower limit of the target size, calculate the excess volume in the scrap steel to be processed that needs to be adjusted; Obtain historical processing records that match the scrap steel type; Based on the historical processing records, the excess volume, and the preset processing efficiency prediction model, the expected processing efficiency is calculated.

4. The method according to claim 2, characterized in that, The step of selecting steel mills that meet the preset conditions from the preset take-home prices as candidate steel mills includes: Based on the preset conditions, determine the price filtering threshold and the distance filtering threshold; The expected net price of each steel mill is compared with the price screening threshold to generate a price comparison result; Based on the price comparison results, a first set of candidate steel mills is selected from all steel mills whose expected delivery price is greater than or equal to the price screening threshold. Based on the steel plant locations of each candidate steel plant in the first candidate steel plant set and the current location, calculate the transportation distance between each candidate steel plant and the scrap steel to be processed. Each of the aforementioned transportation distances is compared with the distance filtering threshold to generate a distance comparison result; Based on the distance comparison results, a second set of candidate steel mills with a transportation distance not greater than the distance screening threshold is selected from the first set of candidate steel mills. If the second candidate steel mill set is not empty, then the steel mill with the highest expected price is selected from the second candidate steel mill set as the alternative steel mill; If the second candidate steel mill set is empty, then the steel mill with the shortest transportation distance is selected from the first candidate steel mill set as the alternative steel mill.

5. The method according to claim 4, characterized in that, The determination of the target steel mill based on the expected processing efficiency and the candidate steel mills includes: Determine whether the number of candidate steel mills is unique; If the number of candidate steel mills is unique, then the candidate steel mill will be used as the target steel mill. If the number of candidate steel mills is not unique, then a preset efficiency threshold is obtained; Compare the expected processing efficiency of the candidate steel mills with the efficiency threshold to determine whether there are any steel mills whose expected processing efficiency is not lower than the efficiency threshold. If there is a steel mill with an expected processing efficiency not lower than the efficiency threshold, then the steel mill with the highest expected processing efficiency is identified as the target steel mill. If there is no steel mill with an expected processing efficiency not lower than the efficiency threshold, then the steel mill with the highest expected final price is selected as the target steel mill.

6. The method according to claim 1, characterized in that, After generating the intelligent decision-making report, the following is also included: The intelligent decision report is sent to the user terminal, and feedback instructions are received from the user terminal. The feedback instruction is type-determined, and a determination result is generated; If the judgment result is a confirmation instruction, then the step of controlling the scrap steel processing equipment to process according to the target processing size is executed; If the determination result is a modification instruction, then extract the user-specified modified data from the modification instruction; If the modified data refers to the modified steel mill, then the modified steel mill is taken as the target steel mill, and the processing size requirements corresponding to the modified steel mill are obtained as the target processing size. If the modified data is the modified processing dimension, then the modified processing dimension shall be used as the target processing dimension, and the original target steel mill shall remain unchanged; The modified data is used to control the scrap steel processing equipment for processing.

7. The method according to claim 2, characterized in that, The method further includes: After the scrap steel processing equipment completes processing according to the target processing size, the actual processing time and the actual amount received are obtained. Calculate the actual processing efficiency based on the actual processing time. The actual processing efficiency is compared with the expected processing efficiency to generate an efficiency deviation. The actual amount received is compared with the expected price received to generate a price deviation. Based on the efficiency deviation, the preset processing efficiency prediction model is updated according to the preset efficiency feedback correction rule to generate an updated prediction model; Based on the price deviation, the preset final price calculation rule is adjusted to generate an adjusted price calculation rule; The updated prediction model and the adjusted price calculation rules are added to a preset correction database for use in the next scrap steel processing calculation.

8. An intelligent decision-making device for scrap steel processing, characterized in that, include: The scrap steel information acquisition module is used to acquire the scrap steel type, basic scrap steel information, and real-time quotations from all steel mills within a preset range that purchase the scrap steel type to be processed. The final price calculation module is used to calculate the expected final price of the scrap steel to be processed based on the real-time price, the basic information of the scrap steel, and the preset final price calculation rules. The alternative steel mill selection module is used to select steel mills that meet the preset conditions from the preset take-home prices as alternative steel mills; The processing requirements acquisition module is used to acquire the processing size requirements of the candidate steel mills; The processing efficiency confirmation module is used to determine the expected processing efficiency of the scrap steel to be processed based on the processing size requirements, the scrap steel type, and the basic information of the scrap steel. The target steel mill determination module is used to determine the target steel mill based on the expected processing efficiency and the candidate steel mills, and to use the processing size requirements of the target steel mill as the target processing size; The decision report generation module is used to generate an intelligent decision report based on the target steel mill, the target processing size, the expected final price, and the expected processing efficiency. The scrap steel processing control module is used to control the scrap steel processing equipment to process the scrap steel to be processed according to the target processing size.

9. An electronic device, characterized in that, Includes a processor, which is coupled to a memory; The processor is configured to execute a computer program stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It includes a computer program or instructions that, when run on a computer, cause the computer to perform the method as described in any one of claims 1 to 7.

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