Concrete raw material comparison and selection method and system

Abstract

The invention provides a concrete raw material comparison and selection method and system, and relates to the technical field of concrete.The concrete raw material comparison and selection method comprises the steps that supplier data and consumer data of concrete raw materials are obtained; performing parameter correction processing according to the supplier data and the consumer data to obtain material parameters; performing performance analysis on the material parameters to obtain performance indexes of the concrete raw materials; according to a supply index, a price index and a performance index of the concrete raw material, predicting through the constructed prediction model to obtain a selection coefficient of the concrete raw material; according to the selection degree coefficient, all the concrete raw materials are sorted, and a comparison and selection result is obtained. According to the comparison and selection method and system provided by the invention, the authenticity and reliability of the comparison and selection result of the concrete raw materials can be ensured.

Description

Technical Field

[0001] This invention relates to the field of concrete technology, and in particular to a method and system for selecting concrete raw materials. Background Technology

[0002] Concrete raw materials mainly include cementitious materials, aggregates, water, and admixtures. By mixing these raw materials in a certain proportion and then going through processes such as stirring, pouring, and curing, concrete with different properties can be formed.

[0003] The existing process for selecting concrete raw materials makes it difficult to accurately and efficiently compare different suppliers' concrete raw materials. Moreover, relying solely on the relatively simple descriptions of concrete raw materials provided by suppliers makes it difficult to accurately analyze the selectivity of concrete raw materials, thus increasing the difficulty of selecting concrete raw materials. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a method and system for selecting concrete raw materials, which solves the problem that the prior art is difficult to accurately and efficiently select concrete raw materials from different suppliers, and is also difficult to accurately analyze the selectivity of concrete raw materials, thus increasing the difficulty of selecting concrete raw materials.

[0005] To achieve the above and other related objectives, this invention provides a method for selecting concrete raw materials, comprising: acquiring supplier data and consumer data of concrete raw materials; performing parameter correction processing based on the supplier data and consumer data to obtain material parameters; performing performance analysis on the material parameters to obtain performance indicators of the concrete raw materials; predicting the selection coefficient of concrete raw materials using a constructed prediction model based on the supply indicators, price indicators, and performance indicators of the concrete raw materials; and ranking all concrete raw materials according to the selection coefficient to obtain the selection result.

[0006] In one embodiment of the present invention, parameter correction processing is performed based on supplier data and consumer data to obtain material parameters, including: obtaining initial parameters of materials supplied by the supplier based on supplier data; and correcting the initial parameters based on consumer data to obtain material parameters.

[0007] In one embodiment of the present invention, the initial parameters include a first initial parameter, a second initial parameter, and remaining initial parameters. Obtaining the initial parameters of the supplier's supplied materials based on supplier data includes: extracting parameters from the supplier data to obtain the first initial parameter; extracting material description data and clarity from the supplier data to obtain the corresponding material grade; obtaining the second initial parameter based on the material grade; obtaining the third parameter type corresponding to the remaining initial parameter based on the first parameter type corresponding to the first initial parameter and the second parameter type corresponding to the second initial parameter; and evaluating the remaining initial parameters based on the third parameter type.

[0008] In one embodiment of the present invention, extracting material specification data and its level of clarity from supplier data to obtain a corresponding material grade includes: obtaining an initial material grade based on the material specification data in the supplier data; and obtaining a material grade based on the level of clarity of the material specification data. Balance factor corresponding to the initial material grade To obtain the level balance When the level is balanced Within the balance threshold range When the material is balanced, the initial material grade is used as the material grade; when the grade balance is balanced... Not within the balance threshold range Within this timeframe, the balance degree and balance threshold range are considered. The corresponding minimum balance value Perform difference calculation to obtain the balance difference value. And based on the balance difference The corresponding downgrade level is obtained, and the initial material level is downgraded according to the downgrade level to obtain the material update level as the material level.

[0009] In one embodiment of the present invention, the remaining initial parameters are evaluated based on the third parameter type, including: the proportion of the third parameter type among all parameter types corresponding to the initial parameters. This yields the supplier preference rating, among which... Indicates the number of types of the third parameter. This indicates the total number of parameter types; based on the supplier's preference level, the adjustment ratio corresponding to each third parameter type is obtained. According to the adjustment ratio The remaining initial parameters are obtained by evaluating the standard initial parameters corresponding to the third parameter type.

[0010] In one embodiment of the present invention, adjusting initial parameters based on consumer data to obtain material parameters includes: analyzing consumer data to extract performance feedback data, wherein the performance feedback data includes positive feedback data and negative feedback data; and obtaining a positively adjustable parameter type based on the positive feedback data. and the corresponding positive regulatory factors Based on the negative feedback data, the type of negative adjustable parameter is obtained. and the corresponding negative regulatory factors According to the type of positively adjustable parameter Positive regulatory factors Negative adjustable parameter type and negative regulators The corresponding initial parameters are corrected to obtain the material parameters.

[0011] In one embodiment of the present invention, the initial parameters include a first initial parameter derived from parameter extraction, a second initial parameter derived from material grade, and remaining initial parameters derived from supplier preference grade; according to the type of positively adjustable parameter... Positive regulatory factors Negative adjustable parameter type and negative regulators The corresponding initial parameters are corrected to obtain material parameters, including: when the positively adjustable parameter type is used. The first initial parameter When the corresponding parameter type is used, the first initial parameter will be... As the first positive correction parameter When the positive adjustable parameter type For the second initial parameter When the corresponding parameter type is used, it is based on the positive adjustment factor. and positive adjustment amount For the second initial parameter The clarity of the corresponding material description data A value-added adjustment is made to obtain a clearly positive correction value. And positively adjust the value according to the degree of clarity. For the second initial parameter Adjustments are made to obtain the second positive correction parameter. When the positive adjustable parameter type For the remaining initial parameters When the corresponding parameter type is used, it is based on the positive adjustment factor. and the corresponding positive parameter correction amount For the remaining initial parameters Adjustments were made to obtain the third positive trimming parameter. When the negative regulatory factor The first initial parameter When the corresponding parameter type is used, it is based on the negative adjustment factor. and the first negative adjustment amount For the first initial parameter Conduct based on maximum clarity The reduction adjustment yielded a clear degree of negative correction value. To obtain the first negative correction parameter When the negative regulatory factor For the second initial parameter When the corresponding parameter type is used, it is based on the negative adjustment factor. Second negative adjustment amount For the second initial parameter The clarity of the corresponding material description data The reduction adjustment yielded a clearly defined negative correction value. And adjust the value negatively according to the degree of clarity. For the second initial parameter Adjustments are made to obtain the second negative correction parameter. When the negative regulatory factor For the remaining initial parameters When the corresponding parameter type is used, it is based on the negative adjustment factor. and the corresponding negative parameter correction amount For the remaining initial parameters Adjustments are made to obtain the third negative trimming parameter. ; the first positive correction parameter Second positive correction parameter Third positive trimming parameter First negative correction parameter Second negative correction parameter Third negative trimming parameter and other fixed initial parameters As material parameters, the remaining fixed initial parameters include the first initial parameter, the second initial parameter, and the uncorrected initial parameter among the remaining initial parameters.

[0012] In one embodiment of the present invention, performance analysis of material parameters is performed to obtain performance indicators of concrete raw materials, including: analyzing material parameters... Compared with standard parameters The difference between them is calculated to obtain the parameter difference value. Among them, material parameters Including the first material parameter Second material parameters Based on parameter differences First material parameters The corresponding first weight and the second material parameters The corresponding second weight Obtain the performance indicators of concrete raw materials .

[0013] In one embodiment of the present invention, the prediction model is constructed in the following manner: obtaining a sample training dataset; training the basic model using the sample training dataset to obtain the prediction model; wherein, the sample training dataset includes a set of supply indicators, a set of price indicators, a set of performance indicators, and corresponding selectivity coefficients for concrete raw materials.

[0014] To achieve the above and other related objectives, the present invention also provides a concrete raw material selection system, comprising: an acquisition unit for acquiring supplier data and consumer data of concrete raw materials; a correction unit for performing parameter correction processing based on the supplier data and consumer data to acquire material parameters; an analysis unit for performing performance analysis on the material parameters to acquire performance indicators of the concrete raw materials; a prediction unit for predicting the selectivity coefficient of concrete raw materials based on the supply indicators, price indicators, and performance indicators of the concrete raw materials through a constructed prediction model; and a sorting unit for sorting all concrete raw materials according to the selectivity coefficient to obtain the selection result.

[0015] As described above, the concrete raw material selection method and system of the present invention has the following beneficial effects: By simultaneously analyzing supplier data and consumer data, the material parameters used to evaluate the performance indicators of concrete raw materials are corrected, which effectively ensures the accuracy of the material parameters used in the concrete raw material selection process, thereby ensuring the conservatism and reliability of the performance indicators of concrete raw materials. Then, based on the corrected material parameters and whether they exceed the parameter standards, corresponding weights are selected to calculate the performance indicators of concrete raw materials, which ensures the comprehensiveness of the performance indicator calculation. Furthermore, based on the supply indicators, price indicators, and performance indicators of concrete raw materials, the selectivity coefficient of relevant concrete raw materials can be quickly determined through a predictive model, thereby determining their selectivity and forming a ranking list, which can help users complete a visual comparison of concrete raw materials. Attached Figure Description

[0016] Figure 1 This is a schematic flowchart of the concrete raw material selection method provided in an embodiment of the present invention.

[0017] Figure 2 The diagram shown is a structural block diagram of a concrete raw material selection system provided in an embodiment of the present invention.

[0018] Figure 3 The diagram shown is a structural schematic of an electronic device according to an embodiment of the present invention.

[0019] Component labeling: Electronic device 1; System 11; Memory 12; Processor 13; Acquisition unit 111; Correction unit 112; Analysis unit 113; Prediction unit 114; Sorting unit 115. Detailed Implementation

[0020] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0021] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0022] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0023] This invention provides a method for comparing and selecting concrete raw materials. By simultaneously utilizing supplier and consumer data to correct material parameters, the accuracy of the material parameters used in the selection process can be effectively ensured, thereby guaranteeing the authenticity of the performance indicators of the concrete raw materials. Furthermore, by combining supply and price indicators of the concrete raw materials, a predictive model is used to derive the selectivity coefficient of the corresponding suppliers. Then, based on the selectivity coefficients of all suppliers, the concrete raw materials from different suppliers are ranked to output the comparison results. This allows users to directly select the concrete raw materials from the specified supplier with the most suitable cost-performance ratio based on the comparison results.

[0024] Figure 1A flowchart illustrating a concrete raw material selection method according to an exemplary embodiment of this application is shown, applied to a concrete raw material selection system, including steps S10-S50. The following will be combined with... Figure 1 The technical solution of this application will be described in detail below.

[0025] First, perform step S10 to obtain supplier and consumer data for concrete raw materials.

[0026] The concrete raw material selection system can obtain supplier data for various concrete raw materials, as well as consumer data after the products are used by consumers. Specifically, the supplier data for concrete raw materials can include parameters provided by suppliers when quoting prices, which can be used directly as initial parameters. The supplier data can also include material specifications explaining the supplied materials, which can be used to describe the initial parameter status for certain parameter types. Of course, the initial parameters for other parameter types cannot be obtained from the supplier data and therefore require further analysis and evaluation. However, supplier data often cannot be used as a direct basis for evaluating the concrete raw material selection results. To improve the accuracy of the selection results, it is necessary to further refine the initial parameters derived from the supplier data analysis based on consumer data such as evaluations after purchase. Utilizing the revised material parameters can fully ensure the authenticity of the performance indicators of the concrete raw materials.

[0027] Next, step S20 is executed to perform parameter correction processing based on supplier data and consumer data to obtain material parameters.

[0028] After obtaining supplier and consumer data through the concrete raw material selection system, initial parameters are first derived from supplier data analysis. Then, based on consumer data, the initial parameters derived from supplier data analysis are further modified to ensure the authenticity and conservatism of the obtained concrete raw material parameters.

[0029] In step S20, parameter correction processing is performed based on supplier data and consumer data to obtain material parameters, including: Step S201: Obtain the initial parameters of the materials supplied by the supplier based on the supplier data; Step S202: Based on consumer data, correct the initial parameters and obtain material parameters.

[0030] In the process of parameter correction using supplier and consumer data, the initial parameters for the materials supplied by the supplier are first determined based on the supplier data. Then, the acquired consumer data is used to further correct the initial parameters, and the corrected parameters are used as the material parameters, thereby improving the authenticity of the material parameters. With accurate material parameters, the authenticity of the performance indicators of concrete raw materials can be further guaranteed, solving the problem of inaccurate selection results of concrete raw materials due to the lack of reliability and conservatism in performance indicators.

[0031] The initial parameters include a first initial parameter, a second initial parameter, and a remaining initial parameter; the first initial parameter is derived based on parameter extraction, the second initial parameter is derived based on material grade, and the remaining initial parameter is derived based on supplier preference grade.

[0032] In step S201, based on the supplier data, the initial parameters of the materials supplied by the supplier are obtained, including: Step S2011: Extract parameters from supplier data to obtain the first initial parameters; Step S2012: Extract the material description data and level of clarity from the supplier's data to obtain the corresponding material grade; Step S2013: Obtain the second initial parameters based on the material grade; Step S2014: Based on the first parameter type corresponding to the first initial parameter and the second parameter type corresponding to the second initial parameter, obtain the third parameter type corresponding to the remaining initial parameters; Step S2015: Evaluate the remaining initial parameters based on the type of the third parameter.

[0033] In calculating the initial parameters of materials supplied by suppliers, the process can begin by extracting parameters from the supplier data to obtain the first set of initial parameters that can be directly extracted. Then, the supplier data may contain material descriptions related to these initial parameters, along with emphasis markers. For example, if the compressive strength of the crushed stone or pebbles meets a certain standard level, it can be classified as having a moderate level of clarity; if the compressive strength of the crushed stone or pebbles has been tested and determined to meet a certain standard level, it indicates a high level of clarity. Therefore, based on the material description data and the level of clarity, the material grade for the corresponding parameter type can be determined, and then the second initial parameter can be calculated based on this material grade. For example, if the compressive strength of the crushed stone or pebbles has been consistently measured and remains stable at a set value, it can be directly used as the first initial parameter. Parameter types without material description data, level of clarity, or corresponding parameter data are directly designated as the third parameter type, and the remaining initial parameters for this third parameter type are evaluated using a unified assessment method.

[0034] In step S2012, the material specification data and level of detail are extracted from the supplier's data to obtain the corresponding material grade, including: Step S20121: Obtain the initial material grade based on the material specification data in the supplier's data; Step S20122: Based on the clarity of the data in the material description Balance factor corresponding to the initial material grade To obtain the level balance ; Step S20123: When the level balance is Within the balance threshold range If the material is used internally, the initial material grade will be used as the material grade. Step S20124: When the level balance is Not within the balance threshold range Within this timeframe, the balance degree and balance threshold range are considered. The corresponding minimum balance value Perform difference calculation to obtain the balance difference value. And based on the balance difference The corresponding downgrade level is obtained, and the initial material level is downgraded according to the downgrade level to obtain the material update level as the material level.

[0035] When determining the material grade for a given parameter type, the initial material grade is first determined based on the material specification data. Furthermore, different material specification data can lead to different initial material grades. For example, if the material specification data specifies a certain standard level, that standard level can be used as the initial material grade, and then the grade can be adjusted based on the specificity of the material specification data. Balance factor corresponding to the initial material grade To obtain the level balance In cases where the data in the material description does not specify a certain standard level, if it is clearly stated that the level was determined experimentally, then the degree of clarity is specified. The degree of certainty is relatively high, but if it is not explicitly stated that it was determined experimentally, then the degree of certainty is... Relatively low. Then, in terms of the balance of levels... Within the balance threshold range When the material is balanced, the initial material grade is used as the material grade. Not within the balance threshold range If the initial material grade is not accurate enough based on the material specification data, it indicates that the initial material grade needs to be lowered to achieve a conservative assessment. Specifically, this is done by adjusting the grade balance and the balance threshold range. The corresponding minimum balance value Perform difference calculation to obtain the balance difference value. And based on the balance difference The corresponding downgrade level is determined, and the initial material level is downgraded based on the downgrade level to obtain the updated material level as the final material level. This method allows for a more conservative assessment of material levels.

[0036] To obtain the initial material grade based on the material description data in the supplier's data, semantic analysis can be used. First, the supplier data is broken down into keywords. Then, information is reorganized based on the set format of the material description data to obtain the material description data. Further analysis of this material description data reveals the corresponding parameter types and their associated parameter data. Based on these parameter types and parameter data, the corresponding initial material grade is determined. Similarly, the corresponding clarity format data can be obtained, and then the clarity of the data can be further determined.

[0037] In step S2015, the remaining initial parameters are evaluated and obtained according to the third parameter type, including: Step S20151: Based on the proportion of the third parameter type among all parameter types corresponding to the initial parameter. This yields the supplier preference rating, among which... Indicates the number of types of the third parameter. Indicates the total number of parameter types; Step S20152: Based on the supplier preference level, obtain the adjustment ratio corresponding to each third parameter type. ; Step S20153: Adjust according to the ratio The remaining initial parameters are obtained by evaluating the standard initial parameters corresponding to the third parameter type.

[0038] When evaluating the remaining initial parameters based on the third parameter type, after determining the first parameter type corresponding to the first initial parameter and the second parameter type corresponding to the second initial parameter, the evaluation is then based on the total number of parameter types. This allows us to further calculate the number of types of the third parameter. Then, based on the proportion of the third parameter type among all parameter types corresponding to the initial parameter, we can determine the order of operations. This is used to categorize suppliers by their preference level. The percentage of each corresponding third parameter type is then determined. The supplier preference level is determined by calculating the range of parameters corresponding to that level. Furthermore, after obtaining the supplier preference level, the adjustment ratio for each third parameter type under that level is also calculated. Finally, based on the adjustment ratio Standard initial parameters corresponding to the third parameter type The remaining initial parameters were evaluated and obtained. The calculation formula is expressed as: .

[0039] In step S202, the initial parameters are corrected based on consumer data to obtain material parameters, including: Step S2021: Analyze consumer data to extract performance feedback data, which includes positive feedback data and negative feedback data; Step S2022: Based on the positive feedback data, obtain the type of positively adjustable parameter. and the corresponding positive regulatory factors ; Step S20223: Based on the negative feedback data, obtain the type of negative adjustable parameter. and the corresponding negative regulatory factors ; Step S2024: Based on the type of positively adjustable parameter Positive regulatory factors Negative adjustable parameter type and negative regulators The corresponding initial parameters are corrected to obtain the material parameters.

[0040] In the process of revising initial parameters based on consumer data, the same semantic analysis method is first used to process the consumer data, thereby extracting performance feedback data that matches the performance feedback data format. This consumer data includes consumer evaluations of concrete raw materials after use. Alternatively, other methods can be used, such as feedback data on concrete raw materials from relevant suppliers obtained through web scraping. After obtaining the performance feedback data, if the performance feedback data is positive, the type of positively adjustable parameters corresponding to the positive feedback data is further determined based on this positive feedback data. Furthermore, based on the semantic strength of positive feedback data, such as words like "good" and "fantastic," corresponding positive moderating factors are derived. Similarly, when the performance feedback data is negative, the type of negative adjustable parameter corresponding to the negative feedback data will be further determined based on the negative feedback data. Furthermore, based on the semantic strength of negative feedback data, such as words like "a bit bad" and "terrible," the corresponding negative moderating factors are derived. Finally, based on the positively adjustable parameter type... Positive regulatory factors Negative adjustable parameter type and negative regulators This allows for the unified correction of all initial parameters to obtain material parameters, thereby ensuring the authenticity of the material parameters and improving the accuracy of performance index evaluation.

[0041] The initial parameters include a first initial parameter derived from parameter extraction, a second initial parameter derived from material grade, and remaining initial parameters derived from supplier preference grade.

[0042] In step S2024, according to the type of positive adjustable parameter Positive regulatory factors Negative adjustable parameter type and negative regulators The corresponding initial parameters are corrected to obtain material parameters, including: Step S20241: When the positive adjustable parameter type The first initial parameter When the corresponding parameter type is used, the first initial parameter will be... As the first positive correction parameter ; Step S20242: When the positive adjustable parameter type For the second initial parameter When the corresponding parameter type is used, it is based on the positive adjustment factor. and positive adjustment amount For the second initial parameter The clarity of the corresponding material description data A value-added adjustment is made to obtain a clearly positive correction value. And positively adjust the value according to the degree of clarity. For the second initial parameter Adjustments are made to obtain the second positive correction parameter. ; Step S20243: When the positive adjustable parameter type For the remaining initial parameters When the corresponding parameter type is used, it is based on the positive adjustment factor. and the corresponding positive parameter correction amount For the remaining initial parameters Adjustments were made to obtain the third positive trimming parameter. ; Step S20244: When the negative adjustment factor The first initial parameter When the corresponding parameter type is used, it is based on the negative adjustment factor. and the first negative adjustment amount For the first initial parameter Conduct based on maximum clarity The reduction adjustment yielded a clear degree of negative correction value. To obtain the first negative correction parameter ; Step S20245: When the negative adjustment factor For the second initial parameter When the corresponding parameter type is used, it is based on the negative adjustment factor. Second negative adjustment amount For the second initial parameter The clarity of the corresponding material description data The reduction adjustment yielded a clearly defined negative correction value. And adjust the value negatively according to the degree of clarity. For the second initial parameter Adjustments are made to obtain the second negative correction parameter. ; Step S20246: When the negative adjustment factor For the remaining initial parameters When the corresponding parameter type is used, it is based on the negative adjustment factor. and the corresponding negative parameter correction amount For the remaining initial parameters Adjustments are made to obtain the third negative trimming parameter. ; Step S20247: Adjust the first positive correction parameter Second positive correction parameter Third positive trimming parameter First negative correction parameter Second negative correction parameter Third negative trimming parameter and other fixed initial parameters As material parameters, the remaining fixed initial parameters include the first initial parameter, the second initial parameter, and the uncorrected initial parameter among the remaining initial parameters.

[0043] Based on positive regulating factors and negative regulators When correcting the initial parameters, there are six cases, namely, positively adjustable parameter types. The first initial parameter The corresponding parameter type is the second initial parameter. The corresponding parameter types and the remaining initial parameters The corresponding parameter type, for negative adjustment factors The first initial parameter The corresponding parameter type is the second initial parameter. The corresponding parameter types and the remaining initial parameters The corresponding parameter type.

[0044] Specifically, in the case of positively adjustable parameter type The first initial parameter When the corresponding parameter type is used, due to the first initial parameter It is a fixed value by itself, so there is no need to make any further corrections, and it is a positively adjustable parameter type. The initial parameters can be further improved. The reliability of the first initial parameter is such that it can be directly used. As the first positive correction parameter .

[0045] When the positive adjustable parameter type For the second initial parameter When the corresponding parameter type is specified, it indicates the current second initial parameter. Further adjustments can be made based on consumer-provided data. That is, by utilizing positive moderating factors. and the corresponding positive adjustment amount To the second initial parameters The clarity of the corresponding material description data A value-added adjustment is made to obtain a clearly positive correction value. This allows for the improvement of the credibility of material description data based on consumer data. Then, a positive correction value is applied based on the level of clarity. For the second initial parameter Adjustments are made to obtain the second positive correction parameter. This achieves the setting of the second initial parameter. Correction processing.

[0046] When the positive adjustable parameter type For the remaining initial parameters When the corresponding parameter type is specified, it indicates that there is consumer data that can prove the remaining initial parameters. The true situation, therefore, can be further investigated through positive regulatory factors. and the corresponding positive parameter correction amount For the remaining initial parameters Adjustments were made to obtain the third positive trimming parameter. This allows for adjustment via the third positive trimming parameter. A more accurate representation of the remaining initial parameters The true situation.

[0047] When negative regulatory factor The first initial parameter When the corresponding parameter type is specified, it indicates that the initial parameter is based on feedback from consumer data. Inaccurate; therefore, the first initial parameter can be determined using consumer data. Further adjustments are needed. That is, by utilizing negative adjustment factors. and the first negative adjustment amount For the first initial parameter Conduct based on maximum clarity The reduction adjustment can then yield a clearly defined negative correction value. Then, based on the degree of clarity, a negative correction value is made. The first negative correction parameter is then obtained. .

[0048] When negative regulatory factor For the second initial parameter When specifying the corresponding parameter type, it should also be stated that the current second initial parameter is based on feedback from consumer data. Inaccurate. Therefore, it is necessary to utilize a negative adjustment factor. Second negative adjustment amount For the second initial parameter The clarity of the corresponding material description data The reduction adjustment yielded a clearly defined negative correction value. And adjust the value negatively according to the degree of clarity. For the second initial parameter Adjustments are made to obtain the second negative correction parameter. .

[0049] When negative regulatory factor For the remaining initial parameters The corresponding parameter type indicates that the remaining initial parameters can be adjusted based on consumer data. Further adjustments and modifications will be made. This involves utilizing negative adjustment factors. and the corresponding negative parameter correction amount For the remaining initial parameters Adjustments are made to allow for the further calculation of the third negative trimming parameter. .

[0050] Finally, the first positive correction parameter obtained from the correction is then... Second positive correction parameter Third positive trimming parameter First negative correction parameter Second negative correction parameter Third negative trimming parameter and other fixed initial parameters , to be used as material parameters.

[0051] Next, step S30 is executed to perform performance analysis on the material parameters and obtain the performance indicators of the concrete raw materials.

[0052] After the material parameters are corrected through the concrete raw material selection system, the material properties formed by all material parameters will be further analyzed to obtain the performance indicators of concrete raw materials, which will serve as the standard for evaluating the performance of concrete raw materials.

[0053] In step S30, performance analysis is performed on the material parameters to obtain the performance indicators of the concrete raw materials, including: Step S301: Set material parameters Compared with standard parameters The difference between them is calculated to obtain the parameter difference value. Among them, material parameters Including the first material parameter Second material parameters ; Step S302: Based on the parameter difference First material parameters The corresponding first weight and the second material parameters The corresponding second weight Obtain the performance indicators of concrete raw materials .

[0054] The first positive correction parameter was obtained by correcting the concrete raw material selection system. Second positive correction parameter Third positive trimming parameter First negative correction parameter Second negative correction parameter Third negative trimming parameter And based on the above parameters, the remaining fixed initial parameters are derived. Then, the first positive correction parameter Second positive correction parameter Third positive trimming parameter First negative correction parameter Second negative correction parameter Third negative trimming parameter and other fixed initial parameters Used as material parameters , respectively with standard parameters By calculating the difference between them, it is possible to achieve the result of exceeding the standard parameter. First material parameters Through formula The corresponding parameter difference is calculated. Then, parameters smaller than the standard parameters will be used. Second material parameter Through formula The corresponding parameter difference is calculated. Finally, based on the first material parameters... The corresponding first weight and the second material parameters The corresponding second weight This allows for further calculation of the performance indicators of concrete raw materials. .

[0055] After determining the performance indicators of concrete raw materials, the price and performance indicators of concrete raw materials can be further determined.

[0056] The price index for concrete raw materials can be determined based on the position of the price of concrete raw materials within the known price range of the entire industry. For example, the price of concrete raw materials is... The index setting factor corresponding to this concrete raw material commodity is: Price indicators of concrete raw materials That is, when the price of concrete raw materials... The higher the price index of concrete raw materials, the higher the price index. The higher.

[0057] For concrete raw material supply indicators, multiple supply conditions can be considered comprehensively, such as the length of the supply route. Supply route length The overall supply difficulty Delivery cycle Influencing factors Calculated supply indicators for concrete raw materials. The formula can be expressed as ,in, Indicate each influencing factor The corresponding influencing factors. In other words, the more factors that influence the supply indicator, the higher the corresponding supply indicator will be.

[0058] Next, step S40 is executed, and the selectivity coefficient of concrete raw materials is predicted by the constructed prediction model based on the supply index, price index and performance index of concrete raw materials.

[0059] To better achieve selective assessment of concrete raw materials, after obtaining the supply, price, and performance indicators of concrete raw materials, the selectivity coefficient of concrete raw materials can be directly predicted in the prediction model constructed by the input values. Thus, the appropriate concrete raw materials can be selected directly based on the selectivity coefficient of concrete raw materials.

[0060] In step S40, the prediction model is constructed as follows: Step S401: Obtain the sample training dataset; Step S402: Train the base model using the sample training dataset to obtain the prediction model; The sample training dataset includes a set of supply indicators, price indicators, and performance indicators for concrete raw materials, as well as corresponding selectivity coefficients.

[0061] In constructing a predictive model, a training dataset can be created using supply, price, and performance indicators of concrete raw materials, along with corresponding selectivity coefficients as labels. This dataset is then used to train the basic model, leading to the predictive model. The calculated supply, price, and performance indicators of the concrete raw materials are then input into the predictive model to predict the corresponding selectivity coefficients.

[0062] Finally, step S50 is executed to sort all concrete raw materials according to the selectivity coefficient to obtain the selection results.

[0063] After obtaining the corresponding selectivity coefficient through the prediction model, the concrete raw materials of all suppliers can be further sorted according to the order of the selectivity coefficient to obtain the corresponding comparison results. Then, according to the user's needs, the concrete raw materials corresponding to the corresponding selectivity coefficient can be selected.

[0064] Please refer to 2. The present invention also provides a concrete raw material selection system 11, comprising: an acquisition unit 111 for acquiring supplier data and consumer data of concrete raw materials; a correction unit 112 for performing parameter correction processing based on supplier data and consumer data to acquire material parameters; an analysis unit 113 for performing performance analysis on material parameters to acquire performance indicators of concrete raw materials; a prediction unit 114 for predicting the selectivity coefficient of concrete raw materials through a constructed prediction model based on the supply indicators, price indicators, and performance indicators of concrete raw materials; and a sorting unit 115 for sorting all concrete raw materials according to the selectivity coefficient to obtain the selection result.

[0065] It should be noted that the concrete raw material selection system 11 provided in the above embodiments and the concrete raw material selection method provided in the above embodiments belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the concrete raw material selection system 11 provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0066] Please see Figure 3 The electronic device 1 may include a memory 12, a processor 13, and a bus, and may also include a computer program stored in the memory 12 and executable on the processor 13, such as a concrete raw material selection program.

[0067] The memory 12 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the electronic device 1, such as a portable hard drive of the electronic device 1. In other embodiments, the memory 12 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 1. Furthermore, the memory 12 can include both internal and external storage units of the electronic device 1. The memory 12 can be used not only to store application software and various types of data installed on the electronic device 1, such as codes for selecting concrete raw materials, but also to temporarily store data that has been output or will be output.

[0068] In some embodiments, the processor 13 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 13 is the control unit of the electronic device 1, connecting various components of the electronic device 1 through various interfaces and lines. It executes programs or modules (such as concrete raw material selection programs) stored in the memory 12, and calls data stored in the memory 12 to perform various functions and process data of the electronic device 1.

[0069] The processor 13 executes the operating system of the electronic device 1 and various installed applications. The processor 13 executes the applications to implement the steps in the above-described concrete raw material selection method.

[0070] For example, the computer program may be divided into one or more modules, which are stored in the memory 12 and executed by the processor 13 to complete this application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the electronic device 1. For example, the computer program may be divided into units in a concrete raw material selection system.

[0071] The integrated unit implemented as a software functional module can be stored in a computer-readable storage medium, which can be non-volatile or volatile. The software functional module, stored in the storage medium, includes several instructions to cause a computer device (which may be a personal computer, a computer device, or a network device, etc.) or a processor to execute some functions of the concrete raw material selection method described in the various embodiments of this application.

[0072] In summary, the concrete raw material selection method and system disclosed in this invention, by simultaneously analyzing supplier and consumer data, corrects the material parameters used to evaluate the performance indicators of concrete raw materials. This effectively ensures the accuracy of the material parameters used in the concrete raw material selection process, thereby guaranteeing the conservatism and reliability of the concrete raw material performance indicators. Then, based on the corrected material parameters and whether they exceed parameter standards, appropriate weights are selected to calculate the performance indicators of the concrete raw materials, ensuring the comprehensiveness of the performance indicator calculation. Furthermore, based on the supply, price, and performance indicators of concrete raw materials, a predictive model can quickly determine the selectivity coefficients of relevant concrete raw materials, forming a ranking list to help users visually compare and select concrete raw materials. Therefore, this invention effectively overcomes the various shortcomings of existing technologies and has high industrial application value.

[0073] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for selecting concrete raw materials, characterized in that, include: Obtain supplier and consumer data for concrete raw materials; Based on the supplier data and the consumer data, parameter correction processing is performed to obtain material parameters; The material parameters are subjected to performance analysis to obtain the performance indicators of the concrete raw materials; Based on the supply indicators, price indicators, and performance indicators of the concrete raw materials, the selectivity coefficient of the concrete raw materials is predicted by the constructed prediction model. Based on the selectivity coefficient, all the concrete raw materials are sorted to obtain the selection results.

2. The method for selecting concrete raw materials according to claim 1, characterized in that, Based on the supplier data and the consumer data, parameter correction processing is performed to obtain material parameters, including: Based on the supplier data, obtain the initial parameters of the materials supplied by the supplier; Based on the consumer data, the initial parameters are corrected to obtain the material parameters.

3. The method for selecting concrete raw materials according to claim 2, characterized in that, The initial parameters include a first initial parameter, a second initial parameter, and remaining initial parameters; Based on the supplier data, obtain the initial parameters of the materials supplied by the supplier, including: The supplier data is used to extract parameters to obtain the first initial parameters; Extract the material description data and its level of clarity from the supplier data to obtain the corresponding material grade; The second initial parameter is obtained based on the material grade; Based on the first parameter type corresponding to the first initial parameter and the second parameter type corresponding to the second initial parameter, the third parameter type corresponding to the remaining initial parameters is obtained; The remaining initial parameters are evaluated based on the type of the third parameter.

4. The method for selecting concrete raw materials according to claim 3, characterized in that, Extract the material description data and its level of clarity from the supplier data to obtain the corresponding material grades, including: The initial material grade is obtained based on the material description data in the supplier data; Based on the clarity of the material description data The balance factor corresponding to the initial material grade To obtain the level balance ; When the level balance Within the balance threshold range If the initial material grade is used, then the initial material grade is taken as the material grade. When the level balance Not within the balance threshold range Within this range, the balance degree of the level and the balance degree threshold range are considered. The corresponding minimum balance value Perform difference calculation to obtain the balance difference value. And based on the balance difference The corresponding downgrade level is obtained, and the initial material level is downgraded according to the downgrade level to obtain the updated material level as the material level.

5. The method for selecting concrete raw materials according to claim 3, characterized in that, Based on the third parameter type, the remaining initial parameters are evaluated and obtained, including: Based on the proportion of the number of all parameter types corresponding to the initial parameter according to the third parameter type. This yields the supplier preference rating, among which... Indicates the number of types of the third parameter. Indicates the total number of parameter types; Based on the supplier preference level, the adjustment ratio corresponding to each of the third parameter types is obtained. ; According to the adjustment ratio The remaining initial parameters are obtained by evaluating the standard initial parameters corresponding to the third parameter type.

6. The method for selecting concrete raw materials according to claim 2, characterized in that, Based on the consumer data, the initial parameters are corrected to obtain the material parameters, including: The consumer data is analyzed to extract performance feedback data, which includes positive feedback data and negative feedback data. Based on the positive feedback data, the type of positively adjustable parameter is obtained. and the corresponding positive regulatory factors ; Based on the negative feedback data, the type of negative adjustable parameter is obtained. and the corresponding negative regulatory factors ; According to the type of positive adjustable parameter The positive regulating factor The negative adjustable parameter type and the negative regulating factor The initial parameters are then corrected to obtain the material parameters.

7. The method for selecting concrete raw materials according to claim 6, characterized in that, The initial parameters include a first initial parameter derived from parameter extraction, a second initial parameter derived from material grade, and remaining initial parameters derived from supplier preference grade; According to the type of positive adjustable parameter The positive regulating factor The negative adjustable parameter type and the negative regulating factor The initial parameters are corrected accordingly to obtain the material parameters, including: When the positive adjustable parameter type The first initial parameter When the corresponding parameter type is used, the first initial parameter is... As the first positive correction parameter ; When the positive adjustable parameter type For the second initial parameter When the corresponding parameter type is used, then the positive adjustment factor is applied. and positive adjustment amount For the second initial parameter The clarity of the corresponding material description data A value-added adjustment is made to obtain a clearly positive correction value. And positively adjust the value according to the stated level of clarity. For the second initial parameter Adjustments are made to obtain the second positive correction parameter. ; When the positive adjustable parameter type For the remaining initial parameters When the corresponding parameter type is used, then the positive adjustment factor is applied. and the corresponding positive parameter correction amount For the remaining initial parameters Adjustments were made to obtain the third positive trimming parameter. ; When the negative adjustment factor The first initial parameter When the corresponding parameter type is used, then the negative adjustment factor is applied. and the first negative adjustment amount For the first initial parameter Conduct based on maximum clarity The reduction adjustment yielded a clear degree of negative correction value. To obtain the first negative correction parameter ; When the negative adjustment factor For the second initial parameter When the corresponding parameter type is used, then the negative adjustment factor is applied. Second negative adjustment amount For the second initial parameter The clarity of the corresponding material description data The reduction adjustment yielded a clearly defined negative correction value. And negatively correct the value according to the stated level of clarity. For the second initial parameter Adjustments are made to obtain the second negative correction parameter. ; When the negative adjustment factor For the remaining initial parameters When the corresponding parameter type is used, then the negative adjustment factor is applied. and the corresponding negative parameter correction amount For the remaining initial parameters Adjustments are made to obtain the third negative trimming parameter. ; The first positive correction parameter The second positive correction parameter The third positive trimming parameter The first negative correction parameter The second negative correction parameter The third negative trimming parameter and other fixed initial parameters As the material parameters, the remaining fixed initial parameters include the first initial parameter, the second initial parameter, and the uncorrected initial parameter among the remaining initial parameters.

8. The method for selecting concrete raw materials according to claim 1, characterized in that, The material parameters are subjected to performance analysis to obtain the performance indicators of the concrete raw materials, including: Material parameters Compared with standard parameters The difference between them is calculated to obtain the parameter difference value. The material parameters Including the first material parameter Second material parameters ; Based on the parameter difference The first material parameters The corresponding first weight and the second material parameter The corresponding second weight To obtain the performance indicators of the concrete raw materials .

9. The method for selecting concrete raw materials according to claim 1, characterized in that, The prediction model is constructed in the following manner: Obtain the sample training dataset; The prediction model is obtained by training the base model using the sample training dataset. The sample training dataset includes a set of supply indicators, a set of price indicators, a set of performance indicators, and corresponding selection coefficients for concrete raw materials.

10. A concrete raw material selection system, characterized in that, include: The acquisition unit is used to acquire supplier data and consumer data for concrete raw materials; The correction unit is used to perform parameter correction processing based on the supplier data and the consumer data to obtain material parameters; An analysis unit is used to perform performance analysis on the material parameters and obtain the performance indicators of the concrete raw materials; The prediction unit is used to predict the selectivity coefficient of the concrete raw materials based on the supply index, price index and performance index of the concrete raw materials through the constructed prediction model. as well as The sorting unit is used to sort all the concrete raw materials according to the selection coefficient to obtain the comparison result.

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