Enterprise transaction risk control system and method based on artificial intelligence
By using an AI-based enterprise transaction risk control system, deep learning is used to predict product damage during transportation and combined with user habits. This solves the problem of the inability to personalize product risk assessment in existing technologies, enabling accurate risk warnings before transactions and improving user experience.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-02-26
- Publication Date
- 2026-05-15
AI Technical Summary
Existing technologies cannot achieve personalized risk assessment of products before transactions, cannot accurately quantify the degradation of product performance during transportation, and cannot dynamically compare the remaining performance of products after delivery with the actual usage needs of users, leading to malfunctions and poor user experience.
An AI-based enterprise transaction risk control system is adopted, which uses deep learning to predict product damage during transportation and combines it with user habit analysis to achieve accurate prediction of product functionality and performance and risk warning.
It enables precise identification and early warning of potential product risks before transactions, improving transaction security and customer satisfaction, and preventing malfunctions and customer complaints.
Smart Images
Figure CN122048035A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of enterprise transaction risk control technology, and in particular to an enterprise transaction risk control system and method based on artificial intelligence. Background Technology
[0002] With the booming development of global e-commerce, online shopping has become a mainstream consumption method. Goods are delivered from warehouses to consumers through complex logistics chains, a process that typically involves multiple handling operations, long-distance transportation, and environmental changes. For many precision electronic products (such as smartphones, laptops, and cameras), precision instruments, or fragile items (such as furniture and ceramics), the stresses caused by vibration, impact, and temperature and humidity fluctuations during transportation are among the main factors leading to the degradation of internal performance and even potential damage. This degradation is often insidious and cannot be immediately detected through visual inspection, but it can significantly shorten the product's lifespan and become a hidden danger for malfunctions in subsequent use.
[0003] Currently, the industry primarily employs the following technical solutions for pre-transaction product risk management: Merchants conduct routine functional testing before shipment and purchase transportation insurance for high-value goods. However, static quality inspection cannot quantify the fatigue accumulation and performance degradation caused by transportation to internal components (such as solder joints, screen connection cables, and battery performance). Insurance models only provide financial compensation after a loss occurs, failing to prevent malfunctions from the source, and the claims process is cumbersome, severely impacting user experience and brand reputation. Some solutions embed simple sensors (such as impact indicators) in packages to record whether excessive impact events have occurred during transportation. This method only provides a binary "yes / no" judgment, unable to quantify chronic damage such as repeated, low-intensity vibrations, and even less able to convert physical monitoring data into predictions of performance degradation for specific product indicators. Some platforms calculate a general failure probability based on historical return and repair data for similar products. This method is too macroscopic and static, ignoring both the specific transportation stress experienced by individual goods and the diverse usage habits and intensities of different users. For example, the risk of malfunction for laptops from the same batch delivered to a light office user and a heavy gamer would be vastly different. Current technology cannot achieve this kind of personalized risk assessment.
[0004] Therefore, existing technologies share a common core flaw: the risk assessment before the transaction is severely disconnected from the actual state of the product after it is delivered to the user and its usage scenarios. Summary of the Invention
[0005] To overcome the shortcomings of existing technologies, this application provides an artificial intelligence-based enterprise transaction risk control system and method. This solution uses artificial intelligence technology to proactively predict product risks before the transaction is completed, utilizes deep learning to accurately quantify the internal performance degradation of the product caused by transportation, and intelligently analyzes users' actual usage habits from their language. It then dynamically compares the remaining performance of the product after delivery with the user's actual usage needs, achieving precise risk positioning and early warning from whether the product will break down to which part will break down under what usage conditions. This helps to avoid failures and complaints at the source, thereby improving transaction security and customer satisfaction.
[0006] To achieve the above objectives, this application adopts the following technical solution:
[0007] Firstly, this application provides an artificial intelligence-based enterprise transaction risk control system, including the following modules:
[0008] The transaction data acquisition module is used to acquire performance data of the traded products and damage to various parts of the products during transportation, and to predict the functional performance of the products to the client.
[0009] The user habit acquisition module is used to acquire users' product usage habits based on user language descriptions.
[0010] The user habit analysis module is used to predict the functional performance of the product from the client end and the user's usage habits of the product.
[0011] The product transaction risk warning module provides product transaction risk warnings based on predictions of product functionality and usage.
[0012] In one implementation of this application, the product-to-client functional performance prediction includes the following specific steps:
[0013] Step 1: Obtain the factory performance data of the traded products during the post-purchase preparation process from the enterprise's ERP, MES (Manufacturing Execution System), or product testing reports. This includes, but is not limited to: core functional parameters (safe usage frequency, safe usage intensity, and standard usage duration); and material property data (such as: tensile strength, corrosion resistance, and color fastness of materials), and store them in the product storage components.
[0014] Step 2: Obtain the average vibration amplitude, vibration frequency and corresponding transportation time during the transportation process of the road section. Based on the material property data of each location, the average vibration amplitude, vibration frequency and corresponding transportation time during the transportation process of the road section, import them into the deep learning neural network model to predict the damage at each location. The damage prediction is the prediction of the degree of deformation at each location, where the degree of deformation is the deformation volume divided by the safe change volume at the corresponding location.
[0015] Step 3: Obtain the damage prediction results for each location, multiply them by the corresponding damage function impact coefficient to obtain the functional damage anomaly results for the corresponding location; the damage function impact coefficient is obtained by averaging historical data, that is, the historical average of the ratio of the corresponding functional decay rate to the damage decay rate. The transportation process is regarded as an important link in the product performance decay, and it is quantitatively predicted. It can expose potential problem products with internal damage but no external damage in advance, which is something that manual inspection cannot do. This achieves accurate tracking from the factory state to the delivery state.
[0016] In one implementation of this application, obtaining the user's product usage habits includes the following specific content:
[0017] Step 1: Obtain user shopping communication data, perform language text preprocessing, and clean the original sentences through word segmentation, error correction, and spoken language standardization;
[0018] Step 2: Use named entity recognition technology to capture the core elements of frequency, intensity, and duration, and then establish behavioral relationships through dependency parsing; First, use a natural language processing model to identify entity units in the text that represent frequency, intensity, and duration.
[0019] Step 3: Construct user habit profiles from the extracted entities. This step can automatically and intelligently extract the core quantitative elements describing user behavior patterns from the text and understand the logical relationships between these elements. This avoids the subjectivity and limitations of traditional questionnaires and can passively and truthfully capture users' deep-seated usage habits from their daily interactions.
[0020] In one implementation of this application, the product's functional usage prediction specifically includes the following:
[0021] Step 1: Obtain the user habit profile, including the frequency, intensity, and duration of user usage at each location, as well as the core functional parameters and abnormal functional damage results of the product.
[0022] Step 2: Subtract the numerical value from the functional impairment abnormality results at each location of the product to obtain the remaining functional coefficient at each location. Multiply the remaining functional coefficient at each location by the core functional parameters of the product to obtain the remaining value of each core functional parameter.
[0023] Step 3: Obtain the remaining values of the core function parameters for each location and compare them with the parameters corresponding to the user habit profile to obtain the usage anomalies of each parameter. The usage anomalies of each parameter are calculated as follows: divide the parameters corresponding to each location in the user habit profile by the remaining values of the core function parameters for each location to obtain the usage anomalies of the parameters for the corresponding location; multiply the usage anomalies of the parameters for the corresponding location to obtain the function usage anomalies for the corresponding location.
[0024] Step four involves comparing the abnormal function usage at each location with the set abnormal function usage threshold. If the abnormal function usage at a corresponding location is greater than or equal to the set abnormal function usage threshold, it indicates that the corresponding location cannot meet the usage requirements. If the abnormal function usage at a corresponding location is less than the set abnormal function usage threshold, it indicates that the corresponding location can meet the usage requirements. By identifying the locations that cannot meet the usage requirements and dynamically comparing the remaining capabilities of the product with the actual needs of the user, a truly personalized risk assessment is achieved. It can not only determine whether the product will break down, but also accurately pinpoint which part will cause problems under what usage habits, making the warning information highly targeted and providing action guidance.
[0025] In one implementation of this application, the product transaction risk warning includes the following specific contents:
[0026] The system determines whether there are locations that cannot meet usage requirements. If such locations exist, a product transaction risk warning is issued to remind users of the risks associated with the specified locations. This may involve prompting users to replace the purchased product or reminding the processing workshop to reinforce the corresponding locations before shipping and informing the users. This directly provides users or merchants with risk insights and solutions, preventing potential product malfunctions and customer complaints.
[0027] Secondly, this application also provides an artificial intelligence-based enterprise transaction risk control method, including the following specific steps:
[0028] Acquire performance data of traded products and damage to various parts of the products during transportation to predict the functional performance of the products to the client.
[0029] Obtain user usage habits for the product based on user language descriptions;
[0030] Based on the product's functional performance prediction from the client to the user and the user's product usage habits, predict the product's functional usage.
[0031] Product transaction risk warnings are issued based on predictions of product functionality and usage.
[0032] Thirdly, this application provides an electronic pipeline comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes an artificial intelligence-based enterprise transaction risk control method by calling the computer program stored in the memory.
[0033] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform an artificial intelligence-based enterprise transaction risk control method.
[0034] Compared with the prior art, this application has the following advantages and beneficial effects:
[0035] This solution uses artificial intelligence technology to proactively predict product risks before the transaction is completed. It uses deep learning to accurately quantify the internal performance degradation of the product caused by transportation, and intelligently analyzes users' actual usage habits from their language. Then, it dynamically compares the remaining performance of the product after delivery with the user's actual usage needs, so as to achieve precise risk positioning and early warning from whether it will break down to which part will break down under what usage conditions. This avoids failures and complaints at the source and improves transaction security and customer satisfaction. Attached Figure Description
[0036] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:
[0037] Figure 1 This is a schematic diagram of the overall process structure of an embodiment of the method of this application;
[0038] Figure 2 This is a schematic diagram of the model structure for predicting functional impairment in an embodiment of the method of this application;
[0039] Figure 3 A schematic diagram of the process structure for using anomaly prediction in the implementation embodiments of the method of this application;
[0040] Figure 4 This is a schematic diagram of the module composition structure of an embodiment of the system in this application. Detailed Implementation
[0041] The technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of this application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.
[0042] Please see Figures 1 to 3 , Figure 1 This is a schematic diagram of the overall process of the enterprise transaction risk control method based on artificial intelligence provided in the embodiments of this application, which specifically includes the following steps:
[0043] Acquire performance data of traded products and damage to various parts of the products during transportation to predict the functional performance of the products to the client.
[0044] In this embodiment, the product-to-client functional performance prediction includes the following specific steps:
[0045] Step 1: Obtain the factory performance data of the traded products during the post-purchase preparation process from the enterprise's ERP, MES (Manufacturing Execution System), or product testing reports. This includes, but is not limited to: core functional parameters (safe usage frequency, safe usage intensity, and standard usage duration); and material property data (such as: tensile strength, corrosion resistance, and color fastness of materials), and store them in the product storage components.
[0046] Step 2: Obtain the average vibration amplitude, vibration frequency, and corresponding transportation time during the transportation process of this road segment. Based on the material property data of each location, the average vibration amplitude, vibration frequency, and corresponding transportation time during the transportation process of the road segment, import them into a deep learning neural network model for damage prediction at each location. Damage prediction is the prediction of the deformation degree at each location, where the deformation degree is the volume of deformation divided by the safe change volume at the corresponding location. The specific steps of the deep learning neural network model are as follows: 1. Model construction: The input layer consists of the corresponding material property data and the average vibration amplitude, vibration frequency, and corresponding transportation time during the transportation process of the road segment; the first hidden layer is a fully connected layer with 128 neurons; the activation function is ReLU to introduce nonlinearity and accelerate convergence; the regularization is 0.3, meaning 30% of the neurons are randomly discarded during training to prevent overfitting; the second hidden layer is also a fully connected layer. The first hidden layer has 64 neurons, with ReLU activation and regularization of 0.3. The third hidden layer is also a fully connected layer with 32 neurons, using ReLU activation and regularization of 0.2. The output layer is also a fully connected layer with one neuron (predicting the degree of deformation at that location), using either Linear activation or ReLU activation. Linear activation is chosen because the degree of deformation can theoretically be any non-negative value, and linear activation gives it a wider output range. If it is certain that the degree of deformation will not be very large, using ReLU can ensure that the output is non-negative, which may make training more stable. 2. Model training configuration: The loss function is mean squared error, because this is the standard loss function for regression problems, and it penalizes predictions that differ significantly from the true value. The optimizer is Adam, because it is an adaptive learning rate algorithm that converges quickly and performs well. The learning rate is set to 0.001 (default value usually works well); the evaluation metric is mean absolute error because it is consistent with the unit of the target variable (degree of deformation) and is easier to interpret; training parameters: batch size is set to 32 to strike a balance between memory efficiency and training stability; number of iterations is 200; validation set split: 20% of the training data is used as the validation set, and the loss on the validation set is monitored to determine overfitting; 3. Model training and evaluation process: the historical dataset is divided into training, validation, and test sets (e.g., 70% / 15% / 15%); a cargo transportation damage model is created according to the above architecture; the model is fitted using the training set data. The training loss is evaluated on a validation set at the end of each batch; the loss curves on the training and validation sets are observed; ideally, both should decrease synchronously and eventually plateau; if the training loss decreases while the validation loss increases, it is a sign of overfitting; after training, a final evaluation is performed on a test set that has never been seen before to obtain the model's generalization performance; the trained model is saved, and when a new transportation task is completed, the model is called to predict damage at various locations on the product; for products with no external damage but internal damage, usage is crucial, as users can only assess whether the external parts are damaged during inspection and cannot obtain the overall damage situation.
[0047] Step 3: Obtain the damage prediction results for each location, multiply them by the corresponding damage-functional impact coefficient to obtain the functional damage anomaly result for that location. The damage-functional impact coefficient is obtained by averaging historical data, specifically the historical average of the ratio of the corresponding functional attenuation rate to the damage attenuation rate. By treating the transportation process as a crucial link in product performance degradation and quantifying it, potential problems with internally damaged but externally undamaged products can be exposed in advance—something manual inspection cannot achieve. This enables precise tracking from the factory state to the delivery state, significantly improving the depth and foresight of quality control. Product damage is directly related to material properties (internal factors) and transportation stress (external factors). Deep learning models are particularly adept at learning such complex nonlinear relationships from historical data.
[0048] Obtain user usage habits for the product based on user language descriptions;
[0049] In this embodiment, obtaining user usage habits of the product includes the following specific details:
[0050] Step 1: Acquire user shopping communication data and perform language text preprocessing. Clean the original sentences through word segmentation, error correction, and colloquial standardization. The specific steps are as follows: First, acquire users' original communication data about shopping. This data is usually colloquial and unstructured natural language. The core task of preprocessing is to deeply clean and standardize it. Utilize word segmentation tools to divide continuous sentences into independent lexical units, ensuring key information is not incorrectly segmented. Simultaneously, use intelligent error correction algorithms to correct spelling errors in the text and normalize internet slang and homophones. Finally, by removing redundant modal particles, parsing referential relationships, and converting colloquial sentence structures such as inverted sentences into standard sentence structures, the final output is standard Chinese text.
[0051] Step 2: Use named entity recognition technology to capture the core elements of frequency, intensity, and duration, and then establish behavioral relationships through dependency parsing; First, use a natural language processing model to identify entity units in the text that represent frequency (e.g., daily), intensity (e.g., usage intensity), and duration (e.g., usage duration);
[0052] Step 3: Construct user habit profiles from the extracted entities. This step can automatically and intelligently extract the core quantitative elements (frequency, intensity, duration) describing user behavior patterns from the text and understand the logical relationship between these elements. This avoids the subjectivity and limitations of traditional questionnaires and can passively and truthfully capture users' deep-seated usage habits from their daily communication.
[0053] Based on the product's functional performance prediction from the client to the user and the user's product usage habits, predict the product's functional usage.
[0054] In this embodiment, the prediction of product function usage specifically includes the following:
[0055] Step 1: Obtain the user habit profile, including the frequency, intensity, and duration of user usage at each location, as well as the core functional parameters and abnormal functional damage results of the product.
[0056] Step 2: Subtract the numerical value from the functional impairment abnormality results at each location of the product to obtain the remaining functional coefficient at each location. Multiply the remaining functional coefficient at each location by the core functional parameters of the product to obtain the remaining value of each core functional parameter.
[0057] Step 3: Obtain the remaining values of the core function parameters for each location and compare them with the parameters corresponding to the user habit profile to obtain the usage anomalies of each parameter. The usage anomalies of each parameter are calculated as follows: divide the parameters corresponding to each location in the user habit profile by the remaining values of the core function parameters for each location to obtain the usage anomalies of the parameters for the corresponding location; multiply the usage anomalies of the parameters for the corresponding location to obtain the function usage anomalies for the corresponding location.
[0058] Step four involves comparing the abnormal function usage at each location with the set abnormal function usage threshold. If the abnormal function usage at a corresponding location is greater than or equal to the set abnormal function usage threshold, it indicates that the corresponding location cannot meet the usage requirements. If the abnormal function usage at a corresponding location is less than the set abnormal function usage threshold, it indicates that the corresponding location can meet the usage requirements. By identifying the locations that cannot meet the usage requirements and dynamically comparing the remaining capacity of the product with the actual needs of the user, a truly personalized risk assessment is achieved. It can not only determine whether the product will break down, but also accurately locate which part will have a problem under what usage habits, making the warning information highly targeted and valuable for action guidance.
[0059] Product transaction risk warnings are issued based on predictions of product functionality and usage.
[0060] In this embodiment, the value schemes of each setting parameter in this application are obtained by fitting historical data. The specific steps are as follows: obtain at least three thousand sets of product transaction data and road transportation data, and at the same time obtain the judgment results of whether the use damage of each part within the service life of the corresponding product can guarantee normal use. Import the product transaction data and road transportation data into this embodiment for specific implementation and obtain the result of abnormal use of the corresponding location. Import the calculation results and judgment results into the MATLAB fitting software for data linear fitting and output the value of the setting parameter that meets the judgment accuracy.
[0061] In this embodiment, the product transaction risk warning includes the following specific contents:
[0062] The system determines whether there are locations that cannot meet usage requirements. If such locations exist, a product transaction risk warning is issued to remind users of the risks associated with the specified locations. This may involve prompting users to replace the purchased product or reminding the processing workshop to reinforce the corresponding locations before shipping and informing the users. This directly provides users or merchants with risk insights and solutions, preventing potential product malfunctions and customer complaints.
[0063] The benefits of this embodiment are as follows: By using artificial intelligence technology, product risks can be proactively predicted before the transaction is completed. Deep learning can be used to accurately quantify the internal performance degradation of the product caused by transportation. At the same time, the user's actual usage habits can be intelligently analyzed from the user's language. Then, the remaining performance of the product after delivery can be dynamically compared with the user's actual usage needs. This enables precise risk positioning and early warning, from whether the product will break down to which part will break down under what usage conditions. This helps to avoid failures and complaints at the source, thereby improving transaction security and customer satisfaction.
[0064] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of an AI-based enterprise transaction risk control system provided in this application embodiment, including:
[0065] The transaction data acquisition module is used to acquire performance data of the traded products and damage to various parts of the products during transportation, and to predict the functional performance of the products to the client.
[0066] The user habit acquisition module is used to acquire users' product usage habits based on user language descriptions.
[0067] The user habit analysis module is used to predict the functional performance of the product from the client end and the user's usage habits of the product.
[0068] The product transaction risk warning module provides product transaction risk warnings based on product usage predictions; the corresponding system module connection structure is as follows: Figure 4 As shown.
[0069] The steps for implementing the corresponding functions of each parameter and unit module in the AI-based enterprise transaction risk control system of this application can be referred to the parameters and steps in the embodiments of the AI-based enterprise transaction risk control method described above, and will not be repeated here.
[0070] Embodiments of this application also provide an electronic conduit, including a memory, a processor, and a communication bus; the memory and the processor are connected via the communication bus. The memory stores an AI-based enterprise transaction risk control method, as provided in the above embodiments, that can be loaded and executed by the processor.
[0071] The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the AI-based enterprise transaction risk control method provided in the above embodiments, etc. The data storage area may store data involved in the AI-based enterprise transaction risk control method provided in the above embodiments, etc.
[0072] A processor may include one or more processing cores. The processor executes instructions, programs, code sets, or instruction sets stored in memory, and calls data stored in memory to perform various functions and process data as described in this application. The processor may be at least one of a specific application-specific integrated circuit, a digital signal processor, a digital signal processing device, a programmable logic device, a field-programmable gate array, a central processing unit, a controller, a microcontroller, and a microprocessor. It is understood that, for different pipelines, the electronic devices used to implement the above-described processor functions may also be other types, and the embodiments of this application do not specifically limit them.
[0073] A communication bus may include a pathway for transmitting information between the aforementioned components. The communication bus can be a PCI bus or an EISA bus, etc. Communication buses can be categorized into address buses, data buses, control buses, etc.
[0074] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments, based on the artificial intelligence-based enterprise transaction risk control method.
[0075] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.
Claims
1. An enterprise transaction risk control system based on artificial intelligence, characterized in that, It includes the following modules: Transaction data acquisition module, which is used to acquire performance data of the traded products and damage to the products at various locations during transportation, and to predict the functional performance of the products to the customer. The user habit acquisition module is used to acquire users' product usage habits based on user language descriptions. The user habit analysis module is used to predict the functional performance of the product from the client end and the user's usage habits of the product. The product transaction risk warning module provides product transaction risk warnings based on predictions of product functionality and usage.
2. The enterprise transaction risk control system based on artificial intelligence according to claim 1, characterized in that, The product-to-customer functional performance prediction includes the following specific steps: Step 1: Obtain the factory performance data of the traded products during the post-purchase inventory preparation process from the enterprise's ERP, MES, or product testing reports; Step 2: Obtain the route information during the transportation process of this section, and import the material property data of each location and the route information during the transportation process into a deep learning neural network model to predict the damage at each location; Step 3: Obtain the damage prediction results at each location, and multiply them by the corresponding damage function influence coefficient to obtain the functional damage anomaly results at the corresponding location.
3. The enterprise transaction risk control system based on artificial intelligence according to claim 1, characterized in that, The acquisition of users' product usage habits includes the following specific details: Step 1: Obtain user shopping communication data, perform language text preprocessing, and clean the original sentences through word segmentation, error correction, and spoken language standardization; Step 2: Use named entity recognition technology to capture core elements such as frequency, intensity, and duration, and then establish behavioral relationships through dependency parsing. Step 3: Build user habit profiles from the extracted entities.
4. The enterprise transaction risk control system based on artificial intelligence according to claim 1, characterized in that, The predicted functional usage of the product includes the following specific details: Step 1: Obtain the user habit profile, including the frequency, intensity, and duration of user usage at each location, as well as the core functional parameters and abnormal functional damage results of the product. Step 2: Subtract the numerical value from the functional impairment abnormality results at each location of the product to obtain the remaining functional coefficient at each location. Multiply the remaining functional coefficient at each location by the core functional parameters of the product to obtain the remaining value of each core functional parameter. Step 3: Obtain the remaining values of the core function parameters of each location and compare them with the parameters corresponding to the user habit profile to obtain the usage anomalies of each parameter. Multiply the usage anomalies of each parameter in the corresponding location to obtain the function usage anomalies of the corresponding location. Step 4: Compare the abnormal function usage data for each location with the set abnormal function usage threshold. If the abnormal function usage data for a corresponding location is greater than or equal to the set abnormal function usage threshold, it means that the corresponding location cannot meet the usage requirements. If the abnormal function usage data for a corresponding location is less than the set abnormal function usage threshold, it means that the corresponding location can meet the usage requirements.
5. The enterprise transaction risk control system based on artificial intelligence according to claim 2, characterized in that, The factory performance data includes core functional parameters and material property data. The travel information includes average vibration amplitude, vibration frequency and corresponding transportation time, which are stored in the product storage component.
6. The enterprise transaction risk control system based on artificial intelligence according to claim 1, characterized in that, The product transaction risk warning includes the following specific contents: The system determines whether there are locations that cannot meet usage requirements. If such locations exist, a product transaction risk warning is issued to remind users of the risks associated with the specified locations. This may involve prompting users to replace the purchased product or reminding the processing workshop to reinforce the corresponding locations before shipping and informing the users. This directly provides users or merchants with risk insights and solutions, preventing potential product malfunctions and customer complaints.
7. The enterprise transaction risk control system based on artificial intelligence according to claim 4, characterized in that, The abnormal usage of the parameters is calculated by dividing each parameter in the user habit profile by the core function parameter of each location and obtaining the remaining value.
8. An artificial intelligence-based enterprise transaction risk control method, implemented based on any one of claims 1-7, characterized in that, Specifically, it includes: Acquire performance data of traded products and damage to various parts of the products during transportation to predict the functional performance of the products to the client. Obtain user usage habits for the product based on user language descriptions; Based on the product's functional performance prediction from the client to the user and the user's product usage habits, predict the product's functional usage. Product transaction risk warnings are issued based on predictions of product functionality and usage.
9. An electronic conduit, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the artificial intelligence-based enterprise transaction risk control method as described in claim 8 by calling the computer program stored in the memory.