Coal mine equipment intelligent transaction management system and method based on big data

Abstract

The invention relates to the technical field of intelligent management, and discloses a coal mine equipment intelligent transaction management system and method based on big data, and the system comprises an intelligent matching module, a transaction execution module, a financial risk control module and a data closed-loop module. A data fusion center is arranged, and when equipment supply and demand dynamic matching is carried out, a cross validation rule of a supplier productivity dynamic portrait and coal mine demand characteristics is constructed, and real-time cooperative calculation of multi-source heterogeneous data is supported, so that a system can identify logic conflicts between supplier parameter updating and purchasing demands in real time, the matching scheme deviation rate is eliminated, and the matching efficiency is improved. The transaction decision accuracy is ensured, the risk of resource mismatching caused by data islands is reduced, and the binding relationship between the delivery action and fund transfer is verified in real time by setting the block chain evidence storage module, so that the system can automatically freeze installment payment when the equipment acceptance is abnormal, the traceability period is shortened, and the efficiency is improved. The problem of credit loss caused by non-traceability of the transaction process is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent management, in particular to a coal mine equipment intelligent transaction management system and method based on big data. BACKGROUND

[0002] A coal mine is an area where coal resources are mined in a coal-rich mining area. It is generally divided into underground coal mines and open-pit coal mines. A coal mine is a reasonable space dug by humans in a geological layer rich in coal. It usually includes roadways, shafts, and mining faces. Coal is the most important solid fuel and is a type of combustible organic rock. It is formed from lush plants that grew in a certain geological era, which were gradually deposited in a thick layer in a suitable geological environment and buried in water or sediment. After a long period of natural coalification, it was formed. As a major energy source to sustain social operations, coal is widely used in industries, chemicals, and transportation. It plays an important role in promoting the normal operation of various departments.

[0003] Currently, since the coal mine equipment transaction process involves multiple subjects, when the equipment supply and demand is dynamically matched, the decentralized storage of supplier inventory data and coal mine procurement demand data cannot be cross-verified in real time. When the supplier temporarily updates the equipment parameters and does not synchronize to the procurement end, it will cause the matching scheme to deviate from the actual demand, and the transaction decision deviation rate is greater than 25%. At the same time, when the intelligent contract executes online transactions, it cannot verify the logical consistency of the logistics delivery status and the fund payment node in real time. When the equipment is accepted and cannot automatically trigger installment payments, it will cause disputes and rely on manual verification of documents, and the dispute resolution period is as long as 42 days. In addition, when generating a logistics-financial coordination scheme, since the transportation cost optimization and financing credit rules belong to independent algorithm modules, it is not possible to realize dynamic path optimization under risk cost constraints. When the low-cost logistics scheme conflicts with the urgent production demand, it is forced to use high-cost express delivery, resulting in a 35% increase in comprehensive expenditure, which seriously affects the accuracy of resource allocation and transaction efficiency.

[0004] Therefore, the present application provides a coal mine equipment intelligent transaction management system and method based on big data to solve the above problems. SUMMARY

[0005] (I) Technical problems solved

[0006] In view of the deficiencies in the prior art, the present application provides a coal mine equipment intelligent transaction management system and method based on big data to solve the problems raised in the background art.

[0007] (II) Technical solutions

[0008] To achieve the above purpose, the present application provides the following technical solutions: a coal mine equipment intelligent transaction management system and method based on big data, the method comprising the following steps:

[0009] S1, collect multi-source heterogeneous data of coal mine equipment transaction, including equipment supplier data, coal mine enterprise demand data, logistics resource data and financial credit data, generate original transaction data set;

[0010] S2, pre-process the original transaction data set, including data cleaning, feature extraction and standardization conversion, generate structured transaction data matrix;

[0011] S3, based on the structured transaction data matrix, construct a device demand-supply matching model through a collaborative filtering algorithm, generate an initial transaction scheme set;

[0012] S4, use intelligent optimization algorithm to multi-objective optimize the initial transaction scheme set, generate the optimal transaction scheme, the multi-objective includes transaction cost, logistics efficiency and financial risk coefficient;

[0013] S5, based on the optimal transaction scheme, call the blockchain smart contract to execute online transaction, real-time generate transaction execution data stream;

[0014] S6, through the big data analysis engine to dynamically monitor the transaction execution data stream, combined with the logistics scheduling algorithm to generate real-time logistics path optimization instruction;

[0015] S7, based on the embedded financial risk control model to assess the transaction fund flow, generate dynamic credit strategy and trigger the financial institution interface;

[0016] S8, aggregate transaction data, logistics data and financial data into platform closed-loop ecological data pool, update the device demand-supply matching model through incremental learning algorithm.

[0017] Preferably, the step S1 comprises:

[0018] S11, collect static data of equipment suppliers through API interface, including equipment model, technical parameters and historical quotation, generate supplier equipment feature vector D s ;

[0019] S12, collect dynamic demand data of coal mine enterprises through demand form, including procurement budget, delivery cycle and technical index constraint, generate demand feature vector R m ;

[0020] S13, get real-time transport capacity data from logistics platform, including vehicle location, carrying capacity and freight unit price, generate logistics resource matrix L t ;

[0021] S14, get enterprise credit rating and financing interest rate data from financial institution interface, generate financial credit vector F c.

[0022] Preferably, the step S2 comprises:

[0023] S21, using a data cleaning algorithm based on a box plot to remove outliers in the original transaction data set, and the cleaning formula is:

[0024]

[0025] Wherein data raw is a single-dimensional data sequence in the original transaction data set, median(·) is an operation of taking the median of data, Q1 and Q3 are the lower quartile and upper quartile of data, IQR is the interquartile range, data clean is the cleaned data point, median(data raw ) is the median of the original data;

[0026] S22, the principal component analysis algorithm is used to reduce the dimension of the cleaned data to generate a structured transaction data matrix M struct .

[0027] Preferably, the implementation of the collaborative filtering algorithm in the step S3 comprises:

[0028] S31, constructing a device-demand similarity matrix W sim , and the calculation formula is:

[0029]

[0030] Wherein R i,k is the rating of demand i on feature k, R j,k is the specification value of device j on feature k, sim(i,j) is the cosine similarity between demand i and j device, is the average of all feature ratings of demand i, is the average specification value of device j, n is the total number of device feature dimensions, i is the demand number of coal mine enterprises, j is the product number of device suppliers, and k is the feature dimension sequence number of the device;

[0031] S32, selecting Top-K matching pairs based on K-nearest neighbor algorithm to generate an initial transaction scheme set S init .

[0032] Preferably, the intelligent optimization algorithm in the step S4 adopts an improved multi-objective fish eagle algorithm, which comprises:

[0033] S41, initializing the fish eagle population position P i =l+r×(u-l), wherein l and u are the lower bound and upper bound of the solution space, r is a random number in [0, 1], and P i is the position of fish eagle individual i;

[0034] S42, update the position in the exploration phase:

[0035] P' i = P i + r x (μ - v x P i )

[0036] wherein μ is the position vector of the optimal solution in the current population, and v is the exploration intensity coefficient;

[0037] S43, update the position in the development phase:

[0038]

[0039] wherein t is the iteration number, P i " is the updated position in the development phase;

[0040] S44, generate the optimal transaction scheme S opt by screening through the Pareto frontier.

[0041] Preferably, the logistics scheduling algorithm in step S6 comprises:

[0042] S61, construct a vehicle path optimization model with time window:

[0043]

[0044] wherein c ij is the transportation cost from node i to j, T i is the actual arrival time, t i is the agreed time window, x ij is the path selection decision variable, α is the unit penalty cost for the length of time violation, and max(T i -t i , 0) is the calculation of the length of time window violation.

[0045] S62, solve the optimal path instruction set R route using the tabu search algorithm.

[0046] Preferably, the construction of the embedded financial risk control model in step S7 comprises:

[0047] S71, calculate the dynamic risk score by a logistic regression model:

[0048]

[0049] wherein risk_score is the dynamic risk score, credit is the credit rating, w1 is the credit rating, w2 is the historical transaction volume, e is the natural constant, and trans_vol is the total transaction amount of the enterprise in the past 12 months.

[0050] S72. When risk_score is less than 0.3, the automatic credit authorization interface is triggered.

[0051] Preferably, the system comprises:

[0052] Data collection module: collects equipment supplier data, coal mining enterprise demand data, logistics resource data and financial credit data through multi-source interfaces;

[0053] Intelligent matching module: including collaborative filtering algorithm engine and multi-objective optimization engine to generate the optimal transaction plan;

[0054] Transaction execution module: executes online transactions by integrating blockchain smart contracts and payment gateways; logistics scheduling module: generates real-time logistics route optimization instructions;

[0055] Financial risk control module: including embedded risk assessment engine and financial institution interface;

[0056] Data closed-loop module: aggregates ecological data and updates the algorithm model through incremental learning.

[0057] Preferably, the intelligent matching module includes:

[0058] Data preprocessing unit: performs outlier cleaning based on box plots and principal component analysis dimensionality reduction;

[0059] Multi-objective optimization unit: Integrates the improved Osprey algorithm to achieve Pareto optimal solution search.

[0060] Preferably, the financial risk control module includes:

[0061] Dynamic credit policy unit: triggers a hierarchical credit mechanism based on risk scores;

[0062] Fund flow monitoring unit: connects to the central bank's credit reporting system and third-party payment platform through real-time data pipeline.

[0063] (3) Beneficial effects

[0064] Compared with the existing technology, the present invention provides a coal mining equipment intelligent transaction management system and method based on big data, which has the following beneficial effects:

[0065] 1. In the present invention, by setting up a data fusion center, when performing dynamic matching of equipment supply and demand, by constructing cross-validation rules for the dynamic portrait of supplier production capacity and coal mine demand characteristics, it supports real-time collaborative calculation of multi-source heterogeneous data, so that the system can identify the logical conflicts between supplier parameter updates and procurement needs in real time, eliminate the deviation rate of matching solutions, ensure the accuracy of transaction decisions, and reduce the risk of resource mismatch caused by data islands.

[0066] 2. In the present application, by setting the blockchain storage module, when the smart contract executes online transactions, the binding relationship between delivery action and fund transfer is verified in real time by analyzing the logistics state data flow and the smart contract trigger condition of the payment node, so that the system can automatically freeze the installment payment when the equipment acceptance is abnormal, shorten the traceability cycle, and solve the credit loss problem caused by the untraceable transaction process.

[0067] 3. In the present application, by setting the collaborative optimization engine, when generating the logistics-financial collaborative scheme, the joint solution model of the transportation cost function and the risk cost constraint is fused to dynamically output the Pareto optimal scheme, so that the system can match low-cost logistics and high-priority credit in an emergency production scenario, avoid high-cost express delivery schemes, and improve the collaborative accuracy of resource scheduling and financial risk control. BRIEF DESCRIPTION OF DRAWINGS

[0068] Figure 1 The figure is a schematic diagram of the overall system architecture of the present application.

[0069] Figure 2 The figure is a flow chart of the method of the present application. DETAILED DESCRIPTION

[0070] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0071] Please refer to Figure 1 The coal mine equipment intelligent transaction management system and method based on big data comprises the following steps:

[0072] S1, collect multi-source heterogeneous data of coal mine equipment transactions, including equipment supplier data, coal mine enterprise demand data, logistics resource data and financial credit data, and generate an original transaction data set;

[0073] S2, pre-process the original transaction data set, including data cleaning, feature extraction and standardization conversion, and generate a structured transaction data matrix;

[0074] S3, based on the structured transaction data matrix, construct a device demand-supply matching model through collaborative filtering algorithm, and generate an initial transaction scheme set;

[0075] S4, use intelligent optimization algorithm to multi-objective optimize the initial transaction scheme set, and generate an optimal transaction scheme, the multi-objective including transaction cost, logistics efficiency and financial risk coefficient;

[0076] S5, based on the optimal transaction scheme, calling the blockchain smart contract to execute the online transaction, and generating transaction execution data stream in real time;

[0077] S6, dynamically monitoring the transaction execution data stream through the big data analysis engine, and generating real-time logistics path optimization instructions combined with the logistics scheduling algorithm;

[0078] S7, based on the embedded financial risk control model, the transaction fund flow is risk assessed, the dynamic credit strategy is generated, and the financial institution interface is triggered;

[0079] S8, the transaction data, logistics data and financial data are aggregated into a platform closed-loop ecological data pool, and the equipment demand-supply matching model is updated through the incremental learning algorithm.

[0080] Step S1 includes:

[0081] S11, collecting static data of equipment suppliers through API interface, including equipment model, technical parameters and historical quotation, generating supplier equipment feature vector D s ;

[0082] S12, collecting dynamic demand data of coal mining enterprises through demand forms, including procurement budget, delivery cycle and technical index constraints, generating demand feature vector R m ;

[0083] S13, obtaining real-time transport capacity data from the logistics platform, including vehicle location, carrying capacity and freight unit price, generating logistics resource matrix L t ;

[0084] S14, obtaining enterprise credit rating and financing interest rate data from the financial institution interface, generating financial credit vector F c .

[0085] Step S2 includes:

[0086] S21, using a data cleaning algorithm based on box plot to remove outliers in the original transaction data set, the cleaning formula is:

[0087]

[0088] Where data raw is a single-dimensional data sequence in the original transaction data set, median(·) is an operation to take the median of the data, Q1 and Q3 are the lower quartile and upper quartile of the data, IQR is the interquartile range, data clean is the cleaned data point, and median(data raw ) is the median of the original data.

[0089] S22, dimension reduction processing is performed on the cleaned data by a principal component analysis algorithm to generate a dimensionally compressed structured transaction data matrix M struct .

[0090] The implementation of the collaborative filtering algorithm in step S3 includes:

[0091] S31, a device-demand similarity matrix W is constructed sim , and the calculation formula is:

[0092]

[0093] where R i,k is the rating of demand i to feature k, R j,k is the specification value of device j at feature k, sim(i,j) is the cosine similarity of demand i and j device, is the average rating of all features of demand i, is the average specification value of device j, n is the total number of device feature dimensions, i is the demand number of the coal mine enterprise, j is the product number of the device supplier, and k is the device feature dimension serial number;

[0094] S32, Top-K matching pairs are selected based on the K-nearest neighbor algorithm to generate an initial transaction scheme set S init .

[0095] In step S4, the intelligent optimization algorithm adopts an improved multi-objective fish eagle algorithm, which includes:

[0096] S41, initialize the fish eagle population position P i =l+r×(u-l), where l and u are the lower and upper bounds of the solution space, r is a random number in [0,1], and P i is the position of fish eagle individual i;

[0097] S42, update the position in the exploration stage:

[0098] P′ i =P i +r×(μ-v×P i )

[0099] where μ is the position vector of the optimal solution in the current population, and v is the exploration intensity coefficient;

[0100] S43, update the position in the development stage:

[0101]

[0102] where t is the iteration number, and P i ″ is the updated position in the development stage;

[0103] S44, the optimal transaction scheme S is generated by screening through the Pareto frontopt .

[0104] The logistics scheduling algorithm in step S6 includes:

[0105] S61, construct a vehicle path optimization model with time window:

[0106]

[0107] Where c ij is the transportation cost from node i to j, T i is the actual arrival time, t i is the agreed time window, x ij is the path selection decision variable, α is the unit penalty cost for default time, max(T i -t i ,0) is the time window default time calculation;

[0108] S62, solve the optimal path instruction set R route .

[0109] The construction of the embedded financial risk control model in step S7 includes:

[0110] S71, calculate the dynamic risk score by a logistic regression model:

[0111]

[0112] Where risk_score is the dynamic risk score, credit is the credit rating, w1 is the credit rating, w2 is the historical transaction volume, e is the natural constant, and trans_vol is the total transaction amount of the enterprise in the past 12 months;

[0113] S72, trigger the automatic credit interface when risk_score is less than 0.3.

[0114] The system includes:

[0115] Data acquisition module: collect equipment supplier data, coal mine enterprise demand data, logistics resource data and financial credit data through multi-source interface;

[0116] Intelligent matching module: including collaborative filtering algorithm engine and multi-objective optimization engine, generating the optimal transaction scheme;

[0117] Transaction execution module: execute online transactions by integrating blockchain smart contract and payment gateway; logistics scheduling module: generate real-time logistics path optimization instructions;

[0118] Financial risk control module: including embedded risk assessment engine and financial institution interface;

[0119] Data closed loop module: aggregate ecological data and update algorithm model through incremental learning.

[0120] Intelligent matching module includes:

[0121] Data preprocessing unit: perform outlier cleaning based on box plot and principal component analysis dimensionality reduction;

[0122] Multi-objective optimization unit: integrate improved fish eagle algorithm to search for Pareto optimal solution.

[0123] Example 1: Intelligent contract and emergency supply linkage:

[0124] A coal mine in Shaanxi needs to urgently reserve flood control equipment before the rainy season, and purchases 10 sets of high-power water pumps. The intelligent contract module monitors the meteorological warning signal in real time, automatically upgrades the procurement priority and triggers the financial pre-credit process: based on the historical credit score of the coal mine and the flood emergency coefficient α = 1.8, the original 30-day account period is compressed to 15 days. When supplier F completes the delivery of the first batch of 5 sets of equipment, the logistics scheduling center simultaneously starts the fish eagle algorithm to optimize the transportation path: the optimal solution is railway trunk line + short-distance highway connection, which reduces the cost by 42% compared with the pure highway solution. 24 hours before the equipment arrives at the mine site, the system automatically activates the installation service reservation module to coordinate the technical team on standby, and finally the equipment is installed and debugged as soon as it arrives, completing the deployment 12 hours before the rain, avoiding the risk of mine flooding, and directly saving potential losses of 23 million yuan.

[0125] Example 2: Data fusion center dynamic matching:

[0126] In the emergency procurement scenario of hydraulic support, a coal mining enterprise requires a bearing pressure greater than 30MPa and a delivery period less than 7 days, while supplier A temporarily updates the equipment parameters from 31.5MPa to 35MPa. After the data fusion center captures the demand vector and equipment vector in real time, it starts the cross-validation rule to calculate the feature matching degree: first identify the delivery period conflict, and then dynamically filter the alternative solution - supplier B equipment automatically fills the position with 92% matching degree. The implementation effect shows that the demand deviation rate is reduced from the industry average of 25% to 4.2%, the procurement cycle is shortened by 40%, and 15 days of production loss caused by parameter mismatch is avoided.

[0127] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0128] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent transaction management of coal mining equipment based on big data, characterized by: The method comprises the following steps: S1. Collect multi-source heterogeneous data on coal mining equipment transactions, including equipment supplier data, coal mining enterprise demand data, logistics resource data, and financial credit data, to generate an original transaction dataset; S2. Preprocessing the original transaction data set, including data cleaning, feature extraction, and standardization conversion, to generate a structured transaction data matrix; S3. Based on the structured transaction data matrix, construct an equipment demand-supply matching model using a collaborative filtering algorithm to generate an initial transaction plan set; S4. Optimizing the initial transaction plan set using an intelligent optimization algorithm for multiple objectives to generate an optimal transaction plan, wherein the multiple objectives include transaction cost, logistics efficiency, and financial risk factor; S5. Based on the optimal transaction solution, call the blockchain smart contract to execute the online transaction and generate the transaction execution data stream in real time; S6. Dynamically monitor the transaction execution data stream using a big data analysis engine and generate real-time logistics route optimization instructions in combination with a logistics scheduling algorithm; S7. Conduct risk assessment on transaction funds flow based on embedded financial risk control models, generate dynamic credit policies and trigger financial institution interfaces; S8. Aggregate transaction data, logistics data, and financial data into a platform closed-loop ecological data pool, and update the equipment demand-supply matching model through an incremental learning algorithm.

2. The method for intelligent transaction management of coal mining equipment based on big data according to claim 1, characterized in that: The step S1 comprises: S11. Collect static data of equipment suppliers through API interface, including equipment model, technical parameters and historical quotation, and generate supplier equipment feature vector D s ; S12. Collect the dynamic demand data of coal mining enterprises through demand forms, including procurement budget, delivery cycle and technical indicator constraints, and generate demand feature vector R m ; S13. Obtain real-time transport capacity data from the logistics platform, including vehicle location, load capacity, and freight unit price, and generate the logistics resource matrix L t ; S14. Obtain enterprise credit rating and financing interest rate data from the financial institution interface to generate a financial credit vector F c .

3. The method for intelligent transaction management of coal mining equipment based on big data according to claim 1, characterized in that: The step S2 comprises: S21. Use a boxplot-based data cleaning algorithm to remove outliers from the original transaction data set. The cleaning formula is: where data raw is a single-dimensional data sequence in the original transaction data set, median(·) is the operation of taking the median of the data, Q1 and Q3 are the lower quartile and upper quartile of the data, IQR is the interquartile range, data clean For the data points after cleaning, median(data raw ) is the median of the original data; S22. Perform dimensionality reduction processing on the cleaned data using the principal component analysis algorithm to generate a dimensionally compressed structured transaction data matrix M. struct .

4. The method for intelligent transaction management of coal mining equipment based on big data according to claim 1, characterized in that: The implementation of the collaborative filtering algorithm in step S3 includes: S31. Constructing equipment-demand similarity matrix W sim , the calculation formula is: where R i,k is the score of requirement i for feature k, R j,k is the specification value of device j in feature k, sim(i,j) is the cosine similarity between requirement i and device j, is the mean of all feature scores of requirement i, is the average specification value of equipment j, n is the total number of equipment characteristic dimensions, i is the coal mining enterprise demand number, j is the equipment supplier product number, and k is the equipment characteristic dimension sequence number; S32. Select the Top-K matching pairs based on the K-nearest neighbor algorithm and generate the initial transaction plan set S init .

5. The method for intelligent transaction management of coal mining equipment based on big data according to claim 1, characterized in that: The intelligent optimization algorithm in step S4 adopts an improved multi-objective Osprey algorithm, including: S41. Initialize the position P of the osprey population i =l+r×(ul), where l and u are the lower and upper bounds of the solution space, r is a random number in [0,1], and P i is the position of osprey individual i; S42, Exploration Phase Update Location: P′ i =P i +r×(μ-v×P i ) Where μ is the position vector of the optimal solution in the current population, and v is the exploration intensity coefficient; S43, Development Phase Update Location: Where t is the number of iterations, P i ″Update location for development phase; S44. Generate the optimal trading plan through Pareto frontier screening opt .

6. The method for intelligent transaction management of coal mining equipment based on big data according to claim 1, characterized in that: The logistics scheduling algorithm in step S6 includes: S61. Construct a vehicle routing optimization model with a time window: where c ij is the transportation cost from node i to j, T i is the actual arrival time, t i is the agreed time window, x ij is the path selection decision variable, α is the penalty cost per unit default duration, max(T i -t i ,0) is the time window breach duration calculation; S62, use the tabu search algorithm to solve the optimal path instruction set R route .

7. The method for intelligent transaction management of coal mining equipment based on big data according to claim 1, characterized in that: The construction of the embedded financial risk control model in step S7 includes: S71. Calculate dynamic risk scores using a logistic regression model: Where risk_score is the dynamic risk score, credit is the credit rating, w1 is the credit rating, w2 is the historical transaction volume, e is a natural constant, and trans_vol is the total transaction volume of the enterprise in the past 12 months; S72. When risk_score is less than 0.3, the automatic credit authorization interface is triggered.

8. A big data-based intelligent transaction management system for coal mining equipment, used to implement the method described in any one of claims 1 to 7, characterized in that: The system comprises: Data collection module: collects equipment supplier data, coal mining enterprise demand data, logistics resource data and financial credit data through multi-source interfaces; Intelligent matching module: including collaborative filtering algorithm engine and multi-objective optimization engine to generate the optimal transaction plan; Transaction execution module: executes online transactions by integrating blockchain smart contracts and payment gateways; logistics scheduling module: generates real-time logistics route optimization instructions; Financial risk control module: including embedded risk assessment engine and financial institution interface; Data closed-loop module: aggregates ecological data and updates the algorithm model through incremental learning.

9. The big data-based intelligent transaction management system for coal mining equipment according to claim 8, characterized in that: The intelligent matching module includes: Data preprocessing unit: performs outlier cleaning based on box plot and principal component analysis dimensionality reduction; Multi-objective optimization unit: Integrate the improved Osprey algorithm to realize Pareto optimal solution search.

10. The big data-based intelligent transaction management system for coal mining equipment according to claim 8, characterized in that: The financial risk control module includes: Dynamic credit policy unit: triggers a graded credit mechanism based on risk scores; Fund flow monitoring unit: connects to the central bank's credit reporting system and third-party payment platform through real-time data pipeline.

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