Steel market order intelligent recommendation method and system based on big data analysis

Abstract

The invention relates to the technical field of order intelligent recommendation, and particularly discloses a steel market order intelligent recommendation method and system based on big data analysis, a user uploads scrap steel information to an intelligent scrap steel resource and order matching platform, and the platform analyzes the storage environment and key attribute information of scrap steel and uses a big data technology and an intelligent algorithm to recommend orders in a steel market. And a proper order is matched, and efficient secondary utilization of the waste steel is achieved. According to the method, the order matching efficiency is improved, the utilization path of scrap steel resources is optimized, and the scrap steel transaction efficiency and success rate are improved. Therefore, the resource waste is reduced, the environmental burden is reduced, and more economic values are created for participants in the waste steel resource market. Through accurate evaluation of scrap steel performance and efficient matching of orders, the platform significantly improves the intelligent level of scrap steel resource management, and promotes sustainable development and technical progress of the scrap steel industry.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent order recommendation, and relates to an intelligent recommendation method and system for steel market orders based on big data analysis. Background Art

[0002] With the rapid development of the steel market, scrap steel, as an important raw material for steel production, has become an important part of recycling and reusing scrap steel to improve resource utilization efficiency and promote the development of a circular economy. However, the traditional order recommendation system has many shortcomings in scrap steel trading: on the one hand, the aging characteristics of scrap steel (such as storage time, environmental impact, etc.) will cause its performance to degrade and affect its reuse value, but the existing technology is difficult to dynamically evaluate the aging degree of scrap steel and incorporate it into order recommendation considerations; on the other hand, scrap steel often presents a mixed nature during the recycling process (such as a mixture of different materials, grades, and pollutants), and the traditional system is difficult to achieve refined demand matching for mixed scrap steel. Summary of the invention

[0003] In view of the above problems existing in the prior art, the present invention provides a method and system for intelligent recommendation of steel market orders based on big data analysis, which are used to solve the above technical problems.

[0004] In order to achieve the above purpose and other purposes, the technical solution adopted by the present invention is as follows: On the one hand, the present invention provides a method for intelligently recommending steel market orders based on big data analysis, the method comprising the following steps: The intelligent scrap steel resource and order matching platform receives the storage information of the target batch of scrap steel to be utilized before recycling, and performs key attribute information detection on the target batch of scrap steel to be utilized; The intelligent scrap steel resource and order matching platform analyzes the uploaded storage environment information, uses the environmental impact model to evaluate the potential impact of storage conditions on the performance of the target batch of scrap steel to be utilized, and generates a storage environment impact score for the target batch of scrap steel to be utilized; The intelligent scrap steel resource and order matching platform classifies the key attribute information of the target batch of scrap steel to be utilized, determines the material grade of the target batch of scrap steel to be utilized, and generates the key attribute score of the target batch of scrap steel to be utilized; The intelligent scrap steel resource and order matching platform searches the existing order database and identifies potential orders that match the target batch of scrap steel information based on the generated storage environment impact score and key attribute score; Based on the order matching results, the intelligent scrap steel resource and order matching platform generates intelligent recommendation plans and pushes matching order information to users.

[0005] The storage information of the target batch of scrap steel to be utilized before recycling includes the storage environment temperature, storage environment humidity, storage environment pollutant concentration and total storage time.

[0006] The key attribute information of the target batch of scrap steel to be utilized is tested. The specific testing process is as follows: The intelligent scrap steel resource and order matching platform obtains key attribute information of the target batch of scrap steel to be utilized through various technical means, wherein the key attribute information includes density, hardness, component content, chemical composition ratio, impurity content and surface damage degree; First, sensor technology is used to collect basic physical and chemical property data of the target batch of scrap steel to be utilized, including density, hardness, and component content; Next, spectral analysis technology is used to detect the elemental composition of the target batch of scrap steel to be utilized, and its chemical composition ratio is accurately identified; In addition, the surface features of the target batch of scrap steel to be utilized are analyzed by image recognition technology to identify the impurity content and the degree of surface damage of the target batch of scrap steel to be utilized.

[0007] Generate the storage environmental impact score of the target batch of scrap steel to be utilized. The specific generation process is as follows: Extract the storage environment temperature and storage environment humidity of the target batch of scrap steel to be utilized before recycling, and estimate the oxidation degree Φ of the target batch of scrap steel to be utilized; Therefore, through the formula , calculate the storage environment impact score S1 of the target batch of scrap steel to be utilized; i is the number of the five single factor parameters: storage environment temperature, storage environment humidity, storage environment pollutant concentration, total storage time and oxidation degree, and i ranges from 1 to 5. is the weight of the i-th single factor parameter, and the single factor parameters are storage environment temperature, storage environment humidity, storage environment pollutant concentration, total storage time and oxidation degree, They represent the weights of storage environment temperature, storage environment humidity, storage environment pollutant concentration, total storage time and oxidation degree respectively; η1 represents the influence weight of the interaction effect between storage environment temperature and storage environment humidity, which is used to determine the intensity of the joint influence of storage environment temperature and storage environment humidity on scrap steel performance. A higher value indicates that the interaction between the two has a greater impact on performance; η2 represents the influence weight of the interaction effect between storage environment pollutant concentration and oxidation degree, which is used to determine the intensity of the joint influence of oxidation degree and pollutant concentration on scrap steel performance. A higher value indicates that the interaction between the two has a greater impact on performance; is the single factor influence function of the i-th single factor parameter.

[0008] The oxidation degree of the target batch of scrap steel to be utilized is estimated by the following specific calculation formula: , ω1, ω2, ω3 and ω4 respectively represent the set weight ratio index, which is used to adjust the influence of each parameter on the degree of oxidation, and ω1+ω2+ω3+ω4=1; C is the material chemical property coefficient of the target batch of scrap steel to be utilized.

[0009] is the single factor influencing function of storage environment temperature; ; is the single factor influencing function of storage environment humidity; ; is the single factor influencing function of the storage environment pollutant concentration; ; It is a single factor influencing function for the total storage duration; ; is the single factor influencing function of oxidation degree; ; is the influence function of the interaction effect between storage environment temperature and storage environment humidity; ; It is the influence function of the interaction effect between the concentration of environmental pollutants and the degree of oxidation; ; In the above formula, Te is the storage environment temperature of the target batch of scrap steel to be utilized before recycling, Hu is the storage environment humidity of the target batch of scrap steel to be utilized before recycling; Da is the total storage time of the target batch of scrap steel to be utilized before recycling, e is a natural constant, and Pc is the storage environment pollutant concentration of the target batch of scrap steel to be utilized before recycling; They are respectively the shapes of the control temperature influence functions, which are specifically explained as adjusting the quadratic effect, linear effect and constant shift of the temperature; To control the intensity of the humidity influence function, the specific function is to adjust the magnitude of the effect of humidity on scrap steel properties; They are respectively the shape of the function that controls the degree of oxidation, To adjust the impact of oxidation, To control the rate of exponential growth; To control the intensity of the storage duration effect function, the specific function is to adjust the impact of the total storage duration on the scrap steel performance; In order to control the intensity of the pollutant concentration influence function, the specific role is to adjust the impact of pollutant concentration on scrap steel performance.

[0010] The logic for generating the key attribute scores of the target batch of scrap steel to be utilized is as follows: Extract the key attribute information of the target batch of scrap steel to be used and generate an attribute vector ,in and is the characteristic value of the jth key attribute information; Use machine learning models to classify the target batch of scrap steel to be utilized, thereby obtaining the material type of the target batch of scrap steel to be utilized , g(·) is the trained classification model; The grade assessment model is used simultaneously to assess the grade of the target batch of scrap steel to be utilized, and the material grade of the target batch of scrap steel to be utilized is obtained; Convert the material type and material grade of the target batch of scrap steel to be utilized into a score, using a weighted scoring model , thereby generating the key attribute score S2 of the target batch of scrap steel to be utilized; σ1 and σ2 are respectively the preset weight influencing factors; Score the material type of the target batch of scrap steel to be utilized. Score the material grade of the target batch of scrap steel to be utilized.

[0011] Identify potential orders that match the target batch of scrap steel to be utilized. The specific identification process is as follows: When searching the existing order database, the intelligent scrap steel resource and order matching platform first calculates the matching score between the scrap steel information and the order requirements: the matching score is calculated based on multiple dimensions: first, material matching. The platform calculates the accuracy of material matching by comparing the key attribute scores of scrap steel with the material requirements of the order; second, storage environment matching. The platform compares the impact score of the scrap steel storage environment with the material performance requirements of the order to evaluate whether the scrap steel meets the order requirements under the current performance state; third, quantity and time matching. The platform analyzes the degree of consistency between the available quantity of scrap steel and the quantity required by the order, while taking into account the delivery time requirements of the order; After calculating the matching degree of each dimension, the platform will weight and combine these matching degrees to generate an overall matching score; The higher the matching score, the more consistent the scrap steel information is with the order requirements; the platform sorts all orders by matching score, identifies potential orders that best match the scrap steel information, and pushes them to users first.

[0012] On the other hand, the present invention provides a steel market order intelligent recommendation system based on big data analysis, including a scrap steel information detection module, an environmental score calculation module, a material score calculation module, an order intelligent matching module and an order intelligent push module; the above modules are connected by wired and / or wireless connection to achieve data transmission between the modules; Scrap steel information detection module: The intelligent scrap steel resource and order matching platform receives the storage information of the target batch of scrap steel to be utilized before recycling, and performs key attribute information detection on the target batch of scrap steel to be utilized; Environmental score calculation module: the intelligent scrap steel resource and order matching platform analyzes the uploaded storage environment information, uses the environmental impact model to evaluate the potential impact of storage conditions on the performance of the target batch of scrap steel to be utilized, and generates a storage environmental impact score for the target batch of scrap steel to be utilized; Material score calculation module: The intelligent scrap steel resource and order matching platform classifies the key attribute information of the target batch of scrap steel to be utilized, determines the material grade of the target batch of scrap steel to be utilized, and generates the key attribute score of the target batch of scrap steel to be utilized; Order intelligent matching module: The intelligent scrap steel resource and order matching platform retrieves the existing order database and identifies potential orders that match the target batch of scrap steel information based on the generated storage environment impact score and key attribute score; Order intelligent push module: Based on the order matching results, the intelligent scrap steel resource and order matching platform generates an intelligent recommendation plan and pushes the matching order information to the user.

[0013] As described above, the steel market order intelligent recommendation method and system based on big data analysis provided by the present invention have at least the following beneficial effects: The present invention systematically processes the storage information and key attribute information of scrap steel through an intelligent scrap steel resource and order matching platform, thereby significantly improving the utilization efficiency of scrap steel resources and the accuracy of order matching. First, the platform receives and analyzes the storage information of the target batch of scrap steel to be used, and uses the environmental impact model to evaluate the potential impact of storage conditions on the performance of scrap steel. This process ensures that the performance changes of scrap steel under different storage conditions can be accurately predicted, thereby generating a reliable storage environmental impact score. This score helps to identify the performance level of scrap steel in its current state, and avoids the impact of performance degradation caused by improper storage on subsequent use; Secondly, the platform classifies and evaluates the key attribute information of the scrap steel to determine its material grade. This step is crucial because scrap steel of different materials has different processing and performance requirements during the recycling process. The generated key attribute scores can help the platform accurately identify the material characteristics of the scrap steel and ensure that the material requirements of the scrap steel for the matching orders can be met; In the order matching stage, the platform searches the existing order database and uses the generated storage environment impact score and key attribute score to identify potential orders that are highly matched with scrap steel information. This intelligent matching process not only improves the efficiency of order matching, but also improves the accuracy of matching, reducing the risk of order failure due to information asymmetry or improper matching.

[0014] Based on the matching results, the platform generates intelligent recommendation solutions and pushes matching order information to users. This function provides users with intuitive and convenient order selection suggestions, saving users time and energy in order search and evaluation. At the same time, through intelligent recommendations, users can find the most suitable orders more quickly, improving the transaction success rate and market circulation speed of scrap steel; In general, the present invention optimizes the utilization path of scrap steel resources and improves the efficiency and success rate of scrap steel transactions through intelligent analysis and matching processes. This not only reduces resource waste and environmental burden, but also creates more economic value for participants in the scrap steel resource market. Through accurate evaluation of scrap steel performance and efficient matching of orders, the platform significantly improves the intelligent level of scrap steel resource management and promotes the sustainable development and technological progress of the scrap steel industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0016] Figure 1 It is a schematic diagram of the connection of each step of the method of the present invention.

[0017] Figure 2 It is a schematic diagram of the connection of various modules of the system of the present invention. DETAILED DESCRIPTION

[0018] The above contents in combination with the implementation of the present invention are merely examples and explanations of the concept of the present invention. The technical personnel in the relevant technical field may make various modifications or supplements to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention. Example

[0019] See also Figure 1 As shown, the steel market order intelligent recommendation method based on big data analysis includes the following steps: The intelligent scrap steel resource and order matching platform receives the storage information of the target batch of scrap steel to be utilized before recycling, and performs key attribute information detection on the target batch of scrap steel to be utilized; The storage information of the target batch of scrap steel to be utilized before recycling includes the storage environment temperature, storage environment humidity, storage environment pollutant concentration and total storage time.

[0020] The key attribute information of the target batch of scrap steel to be utilized is tested. The specific testing process is as follows: The intelligent scrap steel resource and order matching platform obtains key attribute information of the target batch of scrap steel to be utilized through various technical means, wherein the key attribute information includes density, hardness, component content, chemical composition ratio, impurity content and surface damage degree; First, sensor technology is used to collect basic physical and chemical property data of the target batch of scrap steel to be utilized, including density, hardness, and component content; Next, spectral analysis technology is used to detect the elemental composition of the target batch of scrap steel to be utilized, and its chemical composition ratio is accurately identified; In addition, the surface features of the target batch of scrap steel to be utilized are analyzed by image recognition technology to identify the impurity content and the degree of surface damage of the target batch of scrap steel to be utilized.

[0021] The intelligent scrap steel resource and order matching platform analyzes the uploaded storage environment information, uses the environmental impact model to evaluate the potential impact of storage conditions on the performance of the target batch of scrap steel to be utilized, and generates a storage environment impact score for the target batch of scrap steel to be utilized; Generate the storage environmental impact score of the target batch of scrap steel to be utilized. The specific generation process is as follows: Extract the storage environment temperature and storage environment humidity of the target batch of scrap steel to be utilized before recycling, and estimate the oxidation degree of the target batch of scrap steel to be utilized ω1, ω2, ω3 and ω4 represent the set weight ratio index respectively. It is used to adjust the influence of each parameter on the oxidation degree, and ω1+ω2+ω3+ω4=1; C is the material chemical property coefficient of the target batch of scrap steel to be utilized; Therefore, through the formula , calculate the storage environment impact score S1 of the target batch of scrap steel to be utilized; i is the number of the five single factor parameters: storage environment temperature, storage environment humidity, storage environment pollutant concentration, total storage time and oxidation degree, and i ranges from 1 to 5. is the weight of the i-th single factor parameter, and the single factor parameters are storage environment temperature, storage environment humidity, storage environment pollutant concentration, total storage time and oxidation degree, They represent the weights of storage environment temperature, storage environment humidity, storage environment pollutant concentration, total storage time and oxidation degree respectively; η1 represents the influence weight of the interaction effect between storage environment temperature and storage environment humidity, which is used to determine the intensity of the joint influence of storage environment temperature and storage environment humidity on scrap steel performance. A higher value indicates that the interaction between the two has a greater impact on performance; η2 represents the influence weight of the interaction effect between storage environment pollutant concentration and oxidation degree, which is used to determine the intensity of the joint influence of oxidation degree and pollutant concentration on scrap steel performance. A higher value indicates that the interaction between the two has a greater impact on performance; is the single factor influence function of the i-th single factor parameter.

[0022] is the single factor influencing function of storage environment temperature; ; The formula uses a quadratic function to describe the effect of temperature on scrap properties. Temperature is a key factor affecting scrap oxidation and corrosion, and its effect is usually nonlinear. At low temperatures, the oxidation and corrosion rates are low, but as the temperature increases, the chemical reaction rate increases significantly (according to the principle of the Arrhenius equation, the increase in temperature will accelerate the chemical reaction exponentially). Therefore, the quadratic function can better capture the accelerating effect of temperature on performance, while also reflecting the threshold effects that may occur at extremely high temperatures (such as material melting or structural changes). Parameters The settings of the model are adjusted according to experimental data to adapt to different scrap steel materials and storage environment conditions. It can accurately describe the nonlinear effect of temperature on scrap steel properties and avoid the underestimation or overestimation problems that may be caused by linear models. The necessity of this modeling method is that temperature is one of the main driving factors for the change of scrap steel properties, especially in extreme high or low temperature environments, and its impact has a direct and decisive role in the reuse value of scrap steel.

[0023] is the single factor influencing function of storage environment humidity; ; A logarithmic function is used to describe the effect of humidity on scrap properties. Humidity is another key environmental factor that directly affects the oxidation rate and corrosion degree of scrap surface. The higher the humidity, the easier it is for a water film to form on the scrap surface, which promotes electrochemical corrosion reactions. However, the effect of humidity is not a linear relationship. At low humidity (such as 10%-30%), the effect of increased humidity on the corrosion rate is very significant; but at high humidity (such as above 80%), the marginal effect of further increase in humidity on the corrosion rate tends to weaken. This nonlinear characteristic can be well modeled by a logarithmic function, where This avoids the mathematical problems of logarithmic functions when the humidity is zero. It is used to adjust the overall impact of humidity on performance. It can reflect the diminishing marginal effect of humidity and avoid the overestimation problem of linear models. Humidity and temperature together constitute the core influencing factors of scrap steel storage environment. Accurately modeling the impact of humidity is the key to improving the accuracy of scrap steel performance prediction.

[0024] is the single factor influencing function of the storage environment pollutant concentration; ; The square function is used to describe the effect of pollutant concentration on scrap properties. Pollutant concentration Pc (such as acid gases, salts, etc.) is an important external factor affecting scrap corrosion. Low concentrations of pollutants may have little effect on corrosion, but as the concentration of pollutants increases, their aggravating effect on the corrosion rate will show a nonlinear enhancement. This phenomenon can be well modeled by a square function, where (Pc^2) represents the accelerating effect of pollutant concentration on corrosion, and Controls the overall impact of pollutant concentration on performance. It is able to capture the nonlinear exacerbation effect of pollutant concentration on corrosion, especially in highly polluted environments. Pollutant concentration is one of the main drivers of scrap corrosion, and accurately modeling its impact is important for evaluating scrap performance in different pollution environments.

[0025] It is a single factor influencing function for the total storage duration; ; The square root function is used to describe the effect of storage time on scrap steel performance. Storage time Da is an important time dimension parameter that directly affects the aging and performance changes of scrap steel. The performance changes of scrap steel are usually more significant at the beginning of storage, but as time goes by, the degradation rate of performance gradually tends to be flat. The square root function can capture this characteristic well, and its increasing rate is faster when Da is smaller, and gradually slows down when Da is larger. Parameters Used to adjust the magnitude of the effect of time on performance. It can reflect the gradual effect of time on scrap performance and avoid the overestimation problem that may be caused by simple linear models. Storage time is an important variable in the change of scrap performance, and accurate modeling of its impact is particularly important for the performance prediction of long-term storage scrap.

[0026] is the single factor influencing function of oxidation degree; ; An exponential function is used to describe the effect of oxidation degree on scrap steel properties. The oxidation degree Φ represents the depth or coverage of oxidation on the scrap steel surface, and its effect on scrap steel properties is usually nonlinear and accelerated. When the oxidation degree is low, the performance of the scrap steel decreases slowly, but as the oxidation degree increases, the thickness and structure of the oxide layer will significantly affect the mechanical properties and chemical stability of the scrap steel, resulting in rapid deterioration of the performance. The exponential function can well capture this trend of accelerated deterioration, where Controlling the sensitivity of oxidation to performance It is used to adjust the overall impact. It can accurately reflect the accelerated degradation effect of oxidation on scrap steel properties, especially at high oxidation levels. The oxidation degree is a direct indicator of scrap steel performance changes, and its changes have a decisive influence on the physical and chemical properties of scrap steel, so it needs to be accurately modeled through an exponential function.

[0027] is the influence function of the interaction effect between storage environment temperature and storage environment humidity; ; This formula uses the product form to describe the interactive effect of temperature and humidity. Temperature and humidity are the two core driving factors for scrap steel oxidation and corrosion, and the interaction between the two often significantly increases the corrosion rate. For example, in a high temperature and high humidity environment, humidity promotes the formation of water film, and high temperature accelerates the oxidation reaction, resulting in rapid degradation of scrap steel performance. The product form can capture the synergistic effect of the two, which can reflect the joint effect of temperature and humidity on scrap steel performance and avoid the underestimation problem that may be caused by a single factor model. Changes in scrap steel performance are often the result of the joint action of multiple factors, and ignoring the interaction effect may lead to inaccurate prediction models.

[0028] It is the influence function of the interaction effect between the concentration of environmental pollutants and the degree of oxidation; ; This formula combines the product and logarithmic function to describe the interactive effect of oxidation degree and pollutant concentration. Oxidation degree and pollutant concentration are two important factors affecting scrap steel performance. The increase in pollutant concentration will intensify the oxidation reaction, while the increase in oxidation degree will increase the deterioration of pollutants on scrap steel performance. The complex interactive relationship between the two can be captured through the product form, in which the logarithmic function reflects the marginal decreasing effect of pollutant concentration, accurately modeling the joint impact of oxidation and pollutant concentration on scrap steel performance.

[0029] In the above formula, Te is the storage environment temperature of the target batch of scrap steel to be utilized before recycling, Hu is the storage environment humidity of the target batch of scrap steel to be utilized before recycling; Da is the total storage time of the target batch of scrap steel to be utilized before recycling, e is a natural constant, and Pc is the storage environment pollutant concentration of the target batch of scrap steel to be utilized before recycling; They are respectively the shapes of the control temperature influence functions, which are specifically explained as adjusting the quadratic effect, linear effect and constant shift of the temperature; To control the intensity of the humidity influence function, the specific function is to adjust the magnitude of the effect of humidity on scrap steel properties; They are respectively the shape of the function that controls the degree of oxidation, To adjust the impact of oxidation, To control the rate of exponential growth; To control the intensity of the storage duration effect function, the specific function is to adjust the impact of the total storage duration on the scrap steel performance; In order to control the intensity of the pollutant concentration influence function, the specific role is to adjust the impact of pollutant concentration on scrap steel performance.

[0030] The intelligent scrap steel resource and order matching platform classifies the key attribute information of the target batch of scrap steel to be utilized, determines the material grade of the target batch of scrap steel to be utilized, and generates the key attribute score of the target batch of scrap steel to be utilized; The logic for generating the key attribute scores of the target batch of scrap steel to be utilized is as follows: Extract the key attribute information of the target batch of scrap steel to be used and generate an attribute vector ,in and is the characteristic value of the jth key attribute information; Use machine learning models to classify the target batch of scrap steel to be utilized, thereby obtaining the material type of the target batch of scrap steel to be utilized , g(·) is the trained classification model; The grade assessment model is used simultaneously to assess the grade of the target batch of scrap steel to be utilized, and the material grade of the target batch of scrap steel to be utilized is obtained; Convert the material type and material grade of the target batch of scrap steel to be utilized into a score, using a weighted scoring model , thereby generating the key attribute score S2 of the target batch of scrap steel to be utilized; σ1 and σ2 are respectively the preset weight influencing factors; Score the material type of the target batch of scrap steel to be utilized. Score the material grade of the target batch of scrap steel to be utilized.

[0031] The intelligent scrap steel resource and order matching platform searches the existing order database and identifies potential orders that match the target batch of scrap steel information based on the generated storage environment impact score and key attribute score; Identify potential orders that match the target batch of scrap steel to be utilized. The specific identification process is as follows: When searching the existing order database, the intelligent scrap steel resource and order matching platform first calculates the matching score between the scrap steel information and the order requirements. The matching score is calculated based on multiple dimensions: first, material matching. The platform calculates the accuracy of material matching by comparing the key attribute scores of scrap steel with the material requirements of the order; second, storage environment matching. The platform compares the impact score of the scrap steel storage environment with the material performance requirements of the order to evaluate whether the scrap steel meets the order requirements under the current performance state; third, quantity and time matching. The platform analyzes the degree of consistency between the available quantity of scrap steel and the quantity required by the order, while taking into account the delivery time requirements of the order; After calculating the matching degree of each dimension, the platform will weight and combine these matching degrees to generate an overall matching score; The higher the matching score, the more consistent the scrap steel information is with the order requirements; the platform sorts all orders by matching score, identifies potential orders that best match the scrap steel information, and pushes them to users first.

[0032] Based on the order matching results, the intelligent scrap steel resource and order matching platform generates intelligent recommendation plans and pushes matching order information to users. Example

[0033] See also Figure 2 , a steel market order intelligent recommendation system based on big data analysis, including a scrap steel information detection module, an environmental score calculation module, a material score calculation module, an order intelligent matching module and an order intelligent push module; the above modules are connected by wired and / or wireless connection to realize data transmission between the modules; Scrap steel information detection module: The intelligent scrap steel resource and order matching platform receives the storage information of the target batch of scrap steel to be utilized before recycling, and performs key attribute information detection on the target batch of scrap steel to be utilized; Environmental score calculation module: the intelligent scrap steel resource and order matching platform analyzes the uploaded storage environment information, uses the environmental impact model to evaluate the potential impact of storage conditions on the performance of the target batch of scrap steel to be utilized, and generates a storage environmental impact score for the target batch of scrap steel to be utilized; Material score calculation module: The intelligent scrap steel resource and order matching platform classifies the key attribute information of the target batch of scrap steel to be utilized, determines the material grade of the target batch of scrap steel to be utilized, and generates the key attribute score of the target batch of scrap steel to be utilized; Order intelligent matching module: The intelligent scrap steel resource and order matching platform retrieves the existing order database and identifies potential orders that match the target batch of scrap steel information based on the generated storage environment impact score and key attribute score; Order intelligent push module: Based on the order matching results, the intelligent scrap steel resource and order matching platform generates an intelligent recommendation plan and pushes the matching order information to the user.

[0034] All the data in the present invention are normalized to become dimensionless values.

[0035] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0036] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0037] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0038] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent steel market order recommendation method based on big data analysis, characterized in that: include: The intelligent scrap steel resource and order matching platform receives the storage information of the target batch of scrap steel to be utilized before recycling, and performs key attribute information detection on the target batch of scrap steel to be utilized; The intelligent scrap steel resource and order matching platform analyzes the uploaded storage environment information, uses the environmental impact model to evaluate the potential impact of storage conditions on the performance of the target batch of scrap steel to be utilized, and generates a storage environment impact score for the target batch of scrap steel to be utilized; The intelligent scrap steel resource and order matching platform classifies the key attribute information of the target batch of scrap steel to be utilized, determines the material grade of the target batch of scrap steel to be utilized, and generates the key attribute score of the target batch of scrap steel to be utilized; The intelligent scrap steel resource and order matching platform searches the existing order database and identifies potential orders that match the target batch of scrap steel information based on the generated storage environment impact score and key attribute score; Based on the order matching results, the intelligent scrap steel resource and order matching platform generates intelligent recommendation plans and pushes matching order information to users.

2. The method for intelligently recommending steel market orders based on big data analysis according to claim 1 is characterized in that: The storage information of the target batch of scrap steel to be utilized before recycling includes the storage environment temperature, storage environment humidity, storage environment pollutant concentration and total storage time.

3. The method for intelligent recommendation of steel market orders based on big data analysis according to claim 1 is characterized in that: The key attribute information of the target batch of scrap steel to be utilized is tested. The specific testing process is as follows: The intelligent scrap steel resource and order matching platform obtains key attribute information of the target batch of scrap steel to be utilized through various technical means, wherein the key attribute information includes density, hardness, component content, chemical composition ratio, impurity content and surface damage degree; First, sensor technology is used to collect basic physical and chemical property data of the target batch of scrap steel to be utilized, including density, hardness, and component content; Next, spectral analysis technology is used to detect the elemental composition of the target batch of scrap steel to be utilized, and its chemical composition ratio is accurately identified; In addition, the surface features of the target batch of scrap steel to be utilized are analyzed by image recognition technology to identify the impurity content and the degree of surface damage of the target batch of scrap steel to be utilized.

4. The method for intelligently recommending steel market orders based on big data analysis according to claim 2 is characterized in that: Generate the storage environmental impact score of the target batch of scrap steel to be utilized. The specific generation process is as follows: Extract the storage environment temperature and storage environment humidity of the target batch of scrap steel to be utilized before recycling, and estimate the oxidation degree Φ of the target batch of scrap steel to be utilized; Therefore, through the formula , calculate the storage environment impact score S1 of the target batch of scrap steel to be utilized; i is the number of the five single factor parameters: storage environment temperature, storage environment humidity, storage environment pollutant concentration, total storage time and oxidation degree, and i ranges from 1 to 5. is the weight of the i-th single factor parameter, and the single factor parameters are storage environment temperature, storage environment humidity, storage environment pollutant concentration, total storage time and oxidation degree, They represent the weights of storage environment temperature, storage environment humidity, storage environment pollutant concentration, total storage time and oxidation degree respectively; η1 represents the influence weight of the corresponding interaction effect of storage environment temperature and storage environment humidity, which is used to determine the intensity of the joint influence of storage environment temperature and storage environment humidity on scrap steel performance. A higher value indicates that the interaction between the two has a greater impact on the performance; η2 represents the influence weight of the interaction effect between the storage environment pollutant concentration and the oxidation degree, which is used to determine the intensity of the joint influence of the oxidation degree and the pollutant concentration on the performance of scrap steel. A higher value indicates that the interaction between the two has a greater impact on the performance. is the single factor influence function of the i-th single factor parameter.

5. The method for intelligently recommending steel market orders based on big data analysis according to claim 4 is characterized in that: The oxidation degree of the target batch of scrap steel to be utilized is estimated by the following specific calculation formula: , ω1, ω2, ω3 and ω4 respectively represent the set weight ratio index, which is used to adjust the influence of each parameter on the oxidation degree, and ω1+ω2+ω3+ω4=1; C is the material chemical property coefficient of the target batch of scrap steel to be utilized.

6. The method for intelligently recommending steel market orders based on big data analysis according to claim 4 is characterized in that: is the single factor influencing function of storage environment temperature; ; is the single factor influencing function of storage environment humidity; ; is the single factor influencing function of the storage environment pollutant concentration; ; It is a single factor influencing function for the total storage duration; ; is the single factor influencing function of oxidation degree; ; is the influence function of the interaction effect between storage environment temperature and storage environment humidity; ; It is the influence function of the interaction effect between the concentration of environmental pollutants and the degree of oxidation; ; In the above formula, Te is the storage environment temperature of the target batch of scrap steel to be utilized before recycling, Hu is the storage environment humidity of the target batch of scrap steel to be utilized before recycling; Da is the total storage time of the target batch of scrap steel to be utilized before recycling, e is a natural constant, and Pc is the storage environment pollutant concentration of the target batch of scrap steel to be utilized before recycling; They are respectively the shapes of the control temperature influence functions, which are specifically explained as adjusting the quadratic effect, linear effect and constant shift of the temperature; To control the intensity of the humidity influence function, the specific function is to adjust the magnitude of the effect of humidity on scrap steel properties; They are respectively the shape of the function that controls the degree of oxidation, To adjust the impact of oxidation, To control the rate of exponential growth; To control the intensity of the storage duration effect function, the specific function is to adjust the impact of the total storage duration on the scrap steel performance; In order to control the intensity of the pollutant concentration influence function, the specific role is to adjust the impact of pollutant concentration on scrap steel performance.

7. The method for intelligently recommending steel market orders based on big data analysis according to claim 3 is characterized in that: The logic for generating the key attribute scores of the target batch of scrap steel to be utilized is as follows: Extract the key attribute information of the target batch of scrap steel to be used and generate an attribute vector ,in and is the characteristic value of the jth key attribute information; Use machine learning models to classify the target batch of scrap steel to be utilized, thereby obtaining the material type of the target batch of scrap steel to be utilized , g(·) is the trained classification model; The grade assessment model is used simultaneously to assess the grade of the target batch of scrap steel to be utilized, and the material grade of the target batch of scrap steel to be utilized is obtained; Convert the material type and material grade of the target batch of scrap steel to be utilized into a score, using a weighted scoring model , thereby generating the key attribute score S2 of the target batch of scrap steel to be utilized; σ1 and σ2 are respectively the preset weight influencing factors; Score the material type of the target batch of scrap steel to be utilized. Score the material grade of the target batch of scrap steel to be utilized.

8. The method for intelligently recommending steel market orders based on big data analysis according to claim 1, characterized in that: Identify potential orders that match the target batch of scrap steel to be utilized. The specific identification process is as follows: When searching the existing order database, the intelligent scrap steel resource and order matching platform first calculates the matching score between the scrap steel information and the order requirements: the matching score is calculated based on multiple dimensions: the first is the material matching degree, the platform calculates the accuracy of material matching by comparing the scrap steel key attribute score with the order material requirements; the second is the storage environment matching degree, the platform compares the scrap steel storage environment impact score with the order's requirements for material performance, and evaluates whether the scrap steel meets the order requirements under the current performance state; The third is the matching degree between quantity and time. The platform analyzes the matching degree between the available quantity of scrap steel and the quantity required by the order, while taking into account the delivery time requirements of the order; After calculating the matching degree of each dimension, the platform will weight and combine these matching degrees to generate an overall matching score; The higher the matching score, the more consistent the scrap steel information is with the order requirements; the platform sorts all orders by matching score, identifies potential orders that best match the scrap steel information, and pushes them to users first.

9. Intelligent recommendation system for steel market orders based on big data analysis, characterized by: It is implemented based on the steel market order intelligent recommendation method based on big data analysis as described in any one of claims 1 to 8, including a scrap steel information detection module, an environmental score calculation module, a material score calculation module, an order intelligent matching module and an order intelligent push module; the above modules are connected by wired and / or wireless connection to realize data transmission between the modules; Scrap steel information detection module: The intelligent scrap steel resource and order matching platform receives the storage information of the target batch of scrap steel to be utilized before recycling, and performs key attribute information detection on the target batch of scrap steel to be utilized; Environmental score calculation module: the intelligent scrap steel resource and order matching platform analyzes the uploaded storage environment information, uses the environmental impact model to evaluate the potential impact of storage conditions on the performance of the target batch of scrap steel to be utilized, and generates a storage environmental impact score for the target batch of scrap steel to be utilized; Material score calculation module: The intelligent scrap steel resource and order matching platform classifies the key attribute information of the target batch of scrap steel to be utilized, determines the material grade of the target batch of scrap steel to be utilized, and generates the key attribute score of the target batch of scrap steel to be utilized; Order intelligent matching module: The intelligent scrap steel resource and order matching platform retrieves the existing order database and identifies potential orders that match the target batch of scrap steel information based on the generated storage environment impact score and key attribute score; Order intelligent push module: Based on the order matching results, the intelligent scrap steel resource and order matching platform generates an intelligent recommendation plan and pushes the matching order information to the user.

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