Data verification processing method and device

Abstract

The embodiment of the specification provides a data verification processing method and device, wherein the data verification processing method comprises the following steps: in the process of verifying the object text of a transaction object, the object text of the transaction object is input into a processing model to select a data service and generate a service parameter, the data service and the service parameter are obtained, the data interface of the data service is called through the service parameter as an interface input parameter to obtain data, verification data is obtained, the object text and the verification data are input into a large language model to perform data verification, and the verification of the object text of the transaction object is realized by means of the large language model and the data service.

Description

Technical Field

[0001] This document relates to the field of data processing technology, and in particular to a data verification processing method and apparatus. Background Technology

[0002] With the continuous development and promotion of the Internet, the application scope of various online services provided by the Internet is becoming wider and wider. In order to realize asset appreciation and resource management, more and more users are beginning to use online methods to trade and transfer objects. In the process of trading and transferring objects, trading institutions or trading platforms often provide users with certain suggestions. Some of these suggestions are based on the analysis of the trading object itself by the trading institution or trading platform, while others are suggestions to recommend trading objects or trading strategies to users. Summary of the Invention

[0003] This specification provides one or more embodiments of a data verification processing method, comprising: inputting the object text of a transaction object into a processing model to select a data service and generate service parameters, thereby obtaining a data service and service parameters; calling the data interface of the data service using the service parameters as interface input parameters to acquire data and obtain verification data; and inputting the object text and the verification data into a large language model for data verification to obtain a verification result.

[0004] This specification provides one or more embodiments of a data verification processing apparatus, comprising: a service determination module configured to input the object text of a transaction object into a processing model to select a data service and generate service parameters, thereby obtaining a data service and service parameters; a service invocation module configured to invoke the data interface of the data service using the service parameters as interface input parameters to acquire data and obtain verification data; and a data verification module configured to input the object text and the verification data into a large language model for data verification, thereby obtaining a verification result.

[0005] This specification provides one or more embodiments of a data verification processing device, including: a processor; and a memory configured to store computer-executable instructions, which, when executed, cause the processor to: input the object text of a transaction object into a processing model to select a data service and generate service parameters, thereby obtaining a data service and service parameters; use the service parameters as interface input parameters to call the data interface of the data service to acquire data and obtain verification data; and input the object text and the verification data into a large language model for data verification to obtain a verification result.

[0006] This specification provides one or more embodiments of a computer-readable storage medium for storing computer-executable instructions. When executed, these instructions implement the following process: Inputting the object text of a transaction object into a processing model for data service selection and service parameter generation, thereby obtaining the data service and service parameters; Using the service parameters as interface input parameters, calling the data interface of the data service to acquire data and obtain verification data; Inputting the object text and the verification data into a large language model for data verification, thereby obtaining the verification result. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in one or more embodiments of this specification or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 A schematic diagram illustrating the implementation environment of a data verification processing method provided in one or more embodiments of this specification;

[0009] Figure 2 A flowchart illustrating a data verification processing method provided in one or more embodiments of this specification;

[0010] Figure 3 A flowchart illustrating a data verification processing method applied to a data verification system scenario, provided by one or more embodiments of this specification;

[0011] Figure 4 A schematic diagram of an embodiment of a data verification processing device provided in one or more embodiments of this specification;

[0012] Figure 5 This is a schematic diagram of the structure of a data verification processing device provided for one or more embodiments of this specification. Detailed Implementation

[0013] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.

[0014] The data verification processing method provided in one or more embodiments of this specification is applicable to the implementation environment of a data verification system. (Refer to...) Figure 1 The implementation environment includes at least: a data verification module 101, a processing model 102, and a large language model 103; the implementation environment may also include a third-party platform 104.

[0015] Among them, the data verification module 101 is used to generate input data for the input processing model 102 to select data services and generate service parameters, and to generate input data for the large language model 103 to perform data verification, and to call the data interface of the data service provided by the third-party platform 104 to obtain data.

[0016] The processing model 102 is used to select data services and generate service parameters based on the input data input to the data verification module 101, and return the output data services and service parameters to the data verification module 101.

[0017] The large language model 103 is used to perform data verification based on the input data input to the data verification module 101, and return the output verification result to the data verification module 101.

[0018] The third-party platform 104 is used to provide a list of data services to the data verification module 101, and to retrieve data in response to the call of the data verification module 101, and return the retrieved verification data to the data verification module 101.

[0019] In this implementation environment, during the data verification process, the data verification module 101 inputs the object text of the transaction object into the processing model 102. The processing model 102 selects data services and generates service parameters based on the input object text of the transaction object, and returns the obtained data services and service parameters to the data verification module 101. The data verification module 101 uses the service parameters as interface input parameters to call the data interface of the data service provided by the third-party platform 104 to obtain data, and obtains the verification data returned by the interface call. After obtaining the verification data, the data verification module 101 inputs the object text and verification data into the large language model 103. The large language model 103 performs data verification on the input object text and verification data, and returns the obtained verification result to the data verification module 101. In this way, the verification of the object text of the transaction object is achieved by using an external model and an external data interface, ensuring the validity of the object text of the transaction object.

[0020] One or more embodiments of a data verification processing method provided in this specification are as follows:

[0021] Reference Figure 2 The data verification processing method provided in this embodiment specifically includes steps S202 to S206.

[0022] Step S202: Input the object text of the transaction object into the processing model to select data services and generate service parameters, and obtain data services and service parameters.

[0023] The transaction object mentioned in this embodiment refers to a transaction target, tradable object, or tradable product that can be traded, transferred, pledged, or transferred. Transaction objects include financial products, assets, and / or resources; such as various financial products such as currency, valuable metals, foreign exchange, and securities, as well as various forms of assets such as digital assets and data assets, or various forms of resources such as carbon credits (carbon resources).

[0024] Object text refers to the text information generated for a trading object during the trading, circulation, pledging, or transfer of the trading object. This object text can be text information that represents the trading prompts or value changes of the trading object. For example, the object text of a financial product can be the trading keywords of the financial product's historical transactions; another example is the object text of a digital asset, which can be the purchase requirement text that represents the purchase requirements of the digital asset.

[0025] In real-world scenarios, the object text of a transaction reflects the transaction prompts or changes in the value of the transaction object itself. To improve the quality of the object text and prevent low-quality object text from affecting the transaction or circulation of the transaction object, this embodiment improves the quality of the object text by verifying it.

[0026] In practice, during the verification of the object text of a transaction, it is necessary to use verification data obtained from a third party to verify the object text. That is, to use the verification data obtained from a third party to verify the accuracy of the object text, thereby ensuring the quality of the object text of the transaction. Specifically, before obtaining the verification data from the third party, it is necessary to determine which data service to call for data acquisition, and what input parameters are required during the data service call. Here, the object text of the transaction is input into the processing model to select the data service and generate service parameters, thereby obtaining the data service and service parameters. The data service obtained here refers to the data service to be called for data acquisition from the third party, and the service parameters obtained here refer to the input parameters of the data service call during the data acquisition process.

[0027] In determining the data services and service parameters obtained from third parties, to improve the accuracy and flexibility of determining the data services and service parameters, a large language model can be used to determine the data services and service parameters. Specifically, the object text of the transaction object can be input into the large language model to select data services and generate service parameters, thereby obtaining the data services and service parameters.

[0028] In one optional implementation of this embodiment, the object text input processing model of the transaction object is used to select data services and generate service parameters to obtain data services and service parameters, including:

[0029] Generate a first task text containing object text and a list of data services, and input it into a large language model to select data services and obtain data services;

[0030] The second task text for generating data services is input into a large language model to generate service parameters, thus obtaining the service parameters.

[0031] In the specific execution process, when inputting the object text of the transaction object into the large language model for data service selection and service parameter generation, a first task text containing the object text and a list of data services can be generated. This allows the large language model to select the data service required for data verification of the current object text from the list of data services contained in the first task text. By inputting the first task text into the large language model for data service selection, the data service output by the large language model is obtained. Alternatively, a second task text for data services can be generated, allowing the large language model to determine the service parameters of the data service required for data verification of the current object text based on the second task text. By inputting the second task text into the large language model for service parameter generation, the service parameters output by the large language model are obtained.

[0032] For example, if the object text of a transaction is the buy tag "fund inflow" for that transaction, then a first task text is generated based on the buy tag "fund inflow", the data service list, and the task description information (used to describe the task information for selecting data services). After the first task text is input into the big language model, the big language model selects a data service that matches the buy tag "fund inflow" from the data service list according to the task description information in the first task text. The matching data service can be a data service that obtains details of the fund inflow of the transaction.

[0033] After determining the data service, a second task text is generated based on the data service and task description information (used to describe the task information for generating service parameters). After the second task text is input into the large language model, the large language model generates call parameters for calling the current data service to obtain data based on the task description information in the second task text. The output call parameters refer to the service parameters.

[0034] It should be noted that the large language model used for selecting data services and the large language model used for generating service parameters can be the same large language model or two different large language models.

[0035] On the one hand, when the large language model for data service selection and the large language model for service parameter generation are the same large language model, the large language model for data service selection and service parameter generation can be a large language model obtained by fine-tuning a pre-trained large language model or a large language base model using training data (training data for data service selection and service parameter generation). Based on the pre-trained large language model or large language base model, the large language model or large language base model can be fine-tuned for a specific task (Supervised Fine-Tuning, SFT) to obtain a corresponding large language model that can handle specific tasks to adapt to specific domains or task requirements. Here, the large language model obtained after fine-tuning can select data services and generate service parameters based on the input task text and output the corresponding data services and service parameters.

[0036] On the other hand, when the large language model for selecting data services is different from the large language model for generating service parameters, the large language model for selecting data services can be a large language model obtained by fine-tuning a pre-trained large language model or a large language base model using training data (training data for selecting data services). The large language model obtained after fine-tuning can select data services based on the input task text and output the corresponding data services.

[0037] Similarly, the large language model for generating service parameters can also be a large language model obtained by fine-tuning a pre-trained large language model or a large language base model using training data (training data for generating service parameters). The fine-tuned large language model can generate service parameters based on the input task text and output the corresponding service parameters.

[0038] In the specific execution process, during the data service selection process, the large language model can perform word segmentation on the object text to obtain text keywords, and then obtain data services by matching the text keywords with each data service in the data service list; specifically, based on the input of the large language model being the first task text, in one optional implementation method provided in this embodiment, the data service selection includes:

[0039] Semantic recognition is performed on the first task text, and word segmentation is performed on the object text to obtain text keywords;

[0040] Based on the semantic recognition results, the text keywords are matched with each data service in the data service list to obtain the data services.

[0041] In addition, the large language model can also select data services in the following way: perform word segmentation on the object text, perform semantic recognition on the keywords obtained from word segmentation, match the semantic recognition results with each data service in the data service list, and use the matched data service as the data service.

[0042] Furthermore, during the specific execution process, the large language model can perform word segmentation on the object text to obtain text keywords, and generate service parameters by using the object keywords as interface parameters of the service interface; specifically, based on the input of the large language model being the second task text, in one optional implementation of this embodiment, data service selection includes:

[0043] Perform semantic recognition on the second task text and extract the object keywords contained in the second task text;

[0044] Based on the semantic recognition results, the interface parameters of the service interface are generated using object keywords to obtain service parameters.

[0045] It should be noted that, in addition to the above-mentioned implementation method of selecting data services and generating service parameters by inputting the large language model twice in two stages, data service selection and service parameter generation can also be achieved by inputting the large language model once. In another optional implementation method provided in this embodiment, the object text of the transaction object is input into the processing model to select data services and generate service parameters, thereby obtaining data services and service parameters. This includes: generating task text containing object text and a list of data services and inputting it into the large language model to select data services and generate service parameters, thereby obtaining data services and service parameters.

[0046] This includes selecting data services and generating service parameters, such as: performing word segmentation on the object text to obtain text keywords, matching the text keywords with each data service in the data service list to obtain data services, and generating interface parameters of the object keywords in the service interface of the data service to obtain service parameters.

[0047] Specifically, based on the task text as input to the large language model, in one optional implementation of this embodiment, data service selection and service parameter generation are performed, including: semantic recognition of the task text and word segmentation of the object text to obtain text keywords and / or object keywords; matching the text keywords and / or object keywords with each data service in the data service list based on the semantic recognition results to obtain data services; and generating interface parameters for the text keywords and / or object keywords at the service interface based on the semantic recognition results to obtain service parameters.

[0048] In this embodiment, in addition to the above-mentioned implementation method of using a large language model for data service selection and service parameter generation, a pre-trained service decision model specifically for data service selection can be used for data service selection, and a pre-trained parameter generation model specifically for service parameter generation can be used for service parameter generation. In this case, in an optional implementation method provided by this embodiment, the object text of the transaction object is input into the processing model for data service selection and service parameter generation to obtain data services and service parameters, including: inputting the object text into the pre-trained service decision model for data service selection to obtain data services; inputting the object text into the parameter generation model corresponding to the pre-trained data service for service parameter generation to obtain service parameters, or inputting the data service into the pre-trained parameter generation model for service parameter generation to obtain service parameters.

[0049] In addition, during the data service selection and service parameter generation process, a large language model can be combined with a pre-trained service decision model or parameter generation model to select data services and generate service parameters. In another optional implementation provided in this embodiment, the object text of the transaction object is input into the processing model to select data services and generate service parameters to obtain data services and service parameters. This includes: inputting the object text into a pre-trained service decision model to select data services and obtain data services; and inputting the task text containing the data services into a large language model to generate service parameters and obtain service parameters.

[0050] In practical applications, the object text of a transaction object serves to reflect transaction prompts or changes in the object's own value. In this embodiment, to improve the accuracy and effectiveness of object text verification, the object text can be optimized before inputting it into the processing model for data service selection and service parameter generation. Specifically, in one optional implementation, the object text is processed as follows: It is processed according to the object's text processing rules to obtain the processed object text. The text processing rules include denoising rules and / or text standardization rules. In this case, during the object text verification process, verification can be performed based on the object text before processing, or it can be performed based on the processed object text; that is, the object text can be replaced with the processed object text.

[0051] Step S204: Using the service parameters as interface input parameters, call the data interface of the data service to obtain data and acquire verification data.

[0052] In this embodiment, the data service may be provided by a third-party platform, which may be a data platform or a transaction platform. During the verification of the object text of the transaction object, the object text is verified using verification data obtained from the third-party platform. The data service provided by the third-party platform refers to a data acquisition service for obtaining verification data of the object text. Specifically, it may be a data acquisition service for obtaining object-related data and / or transaction-related data of the transaction object. With the obtained object-related data and / or transaction-related data, the object text of the transaction object can be verified. Optionally, the data service includes: a data acquisition service for obtaining object-related data and / or transaction-related data of the transaction object.

[0053] Among them, service parameters refer to the input parameters of the data interface during the process of calling the data service data interface to obtain data; optional, service parameters include at least one of the following: object name, object attribute, time parameter.

[0054] In practice, the data service that determines the verification data of the object text of the transaction object is determined above. Based on the service parameters of the current data service, the data service's data interface is called with the service parameters as the interface input parameters to obtain data. The data returned by the interface call is the verification data for verifying the object text of the transaction object.

[0055] For example, when the object text of a transaction is the buy tag "fund inflow", during the verification of the buy tag "fund inflow", the large language model selects the data service that matches the buy tag "fund inflow" from the data service list. Based on the call parameters generated by the large language model for the current data service to obtain data, the generated call parameters are used as the interface call input parameters to call the service interface of the current data service to obtain the fund inflow details of the transaction. The fund inflow details of the transaction can then be used to verify the buy tag "fund inflow" of the transaction.

[0056] Step S206: Input the object text and the verification data into the large language model for data verification and obtain the verification result.

[0057] In practice, after obtaining the verification data by calling the service interface of the data service, that is, after obtaining the verification data for verifying the object text of the transaction object, the object text and the verification data are input into the large language model for data verification. Specifically, the object text and the verification data are input into the large language model so that the large language model can use the verification data to verify the object text and obtain the verification result of the object text.

[0058] In one optional implementation of this embodiment, the object text and verification data are input into a large language model for data verification to obtain verification results, including:

[0059] Generate a task text containing verification task information, object text, and verification data;

[0060] Input the task text into the large language model to verify the object text based on the verification data and obtain the verification results.

[0061] Specifically, during the data verification process, the large language model can perform semantic recognition on the task text, extract features from the object text and verification data based on the semantic recognition results to obtain text features and verification data features, and perform consistency verification based on the text features and verification data features. If the consistency verification based on the text features and verification data features passes, a successful verification result is output; otherwise, if the consistency verification based on the text features and verification data features fails, a failed verification result is output.

[0062] It should be noted that the large language model used for data verification can be the same as the large language model used for data service selection and / or service parameter generation. In addition, the large language model used for data verification can also be the same as the large language model used for data service selection and service parameter generation.

[0063] In summary, the data verification processing method provided in this embodiment verifies the object text of a transaction object by inputting the object text of the transaction object into a processing model to select data services and generate service parameters. This obtains the data service needed to acquire verification data for verifying the object text of the transaction object, as well as the service parameters needed to call the data service to acquire data. The data service's data interface is then called with the service parameters as interface input parameters to acquire data. The data returned by the interface call is the verification data for verifying the object text of the transaction object. Finally, by inputting the object text and verification data into a large language model, the large language model can verify the object text based on the verification data to obtain the verification result. This verifies the accuracy of the object text by calling the data service, thereby ensuring the quality of the object text of the transaction object.

[0064] Furthermore, in the process of determining data services and service parameters, a large language model can also be used to determine data services and service parameters. Specifically, the object text of the transaction object can be input into the large language model to select data services and generate service parameters, thereby obtaining data services and service parameters, thus improving the accuracy and flexibility of determining data services and service parameters.

[0065] The following example uses a data verification processing method provided in this embodiment in a data verification system scenario as an example, combined with... Figure 3 The data verification processing method provided in this embodiment will be further explained below. See [link to documentation]. Figure 3 The data verification processing method applied to data verification system scenarios includes the following steps.

[0066] Step S302: Process the object text according to the text processing rules of the transaction object to obtain the processed object text.

[0067] Step S304: Generate a first task text containing the processed object text and a list of data services, and input it into the large language model to select data services and obtain data services.

[0068] Step S306: Generate the second task text for the data service and input it into the large language model to generate service parameters, thereby obtaining the service parameters.

[0069] Step S308: Use the service parameters as interface input parameters to call the data service's data interface to obtain data and acquire verification data.

[0070] Step S310: Generate a third task text containing verification task information, processed object text, and verification data.

[0071] Step S312: Input the third task text into the large language model to verify the object text based on the verification data and obtain the verification result.

[0072] It should be noted that the data verification system may consist of a text verification module, a service selection proxy module, a parameter generation proxy module, a service invocation module, and / or a verification proxy module;

[0073] During the execution of step S304, the text verification module can generate a first task text containing the processed object text and a list of data services, and send a request carrying the first task text to the service selection agent module. In response to the request, the service selection agent module inputs the first task text into the big language model to select data services, and returns the data services obtained by the big language model to the text verification module.

[0074] During the execution of step S306, the text verification module can generate the second task text of the data service and send a request carrying the second task text to the parameter generation agent module. In response to the request, the parameter generation agent module inputs the second task text into the large language model to generate service parameters and returns the service parameters obtained by the large language model to the text verification module.

[0075] During the execution of step S308, the text verification module can send a request carrying service parameters to the service call module. In response to the request, the service call module calls the data interface of the data service with the service parameters as the interface input parameters to obtain data, and returns the verification data obtained by the interface call to the text verification module.

[0076] During the execution of step S312, the text verification module may send a request carrying the third task text to the verification agent module. In response to the request, the verification agent module inputs the third task text into the large language model for data verification and returns the verification result obtained by the large language model to the text verification module.

[0077] It should be noted that any one or more steps in steps S302 to S312 can be combined with any one or more steps in steps S202 to S206 to form a new implementation method according to the needs of implementation and deployment. In addition, any one or more technical features in steps S302 to S312 can be selected and combined with any one or more technical features provided in steps S202 to S206 to form a new implementation method according to the actual deployment needs. Alternatively, any one or more technical features in steps S302 to S312 can be replaced with any one or more technical features provided in steps S202 to S206 to form a new implementation method according to the actual deployment needs. These will not be elaborated on here.

[0078] The following is an embodiment of a data verification and processing device provided in this specification:

[0079] In the above embodiments, a data verification processing method is provided, and correspondingly, a data verification processing device is also provided, which will be described below with reference to the accompanying drawings.

[0080] Reference Figure 4 The diagram shows a schematic representation of a data verification processing device embodiment provided in this embodiment.

[0081] Since the apparatus embodiments correspond to the method embodiments, the descriptions are relatively simple. For relevant parts, please refer to the corresponding descriptions of the method embodiments provided above. The apparatus embodiments described below are merely illustrative.

[0082] This embodiment provides a data verification processing device, the device comprising:

[0083] The service determination module 402 is configured to input the object text of the transaction object into the processing model to select data services and generate service parameters, thereby obtaining data services and service parameters.

[0084] Service call module 404 is configured to call the data interface of the data service with the service parameters as interface input parameters to obtain data and acquire verification data.

[0085] The data verification module 406 is configured to input the object text and the verification data into a large language model for data verification and obtain the verification result.

[0086] The following is an example of a data verification and processing device provided in this specification:

[0087] Corresponding to the data verification processing method described above, based on the same technical concept, one or more embodiments of this specification also provide a data verification processing device, which is used to execute the data verification processing method provided above. Figure 5 This is a schematic diagram of the structure of a data verification processing device provided for one or more embodiments of this specification.

[0088] This embodiment provides a data verification processing device, including:

[0089] like Figure 5 As shown, the data verification processing device can vary significantly due to differences in configuration or performance. It may include one or more processors 501 and memory 502, and the memory 502 may store one or more application programs or data. The memory 502 may be temporary or persistent storage. The application programs stored in the memory 502 may include one or more modules (not shown in the figure), and each module may include a series of computer-executable instructions in the data verification processing device. Furthermore, the processor 501 may be configured to communicate with the memory 502 and execute the series of computer-executable instructions in the memory 502 on the data verification processing device. The data verification processing device may also include one or more power supplies 503, one or more wired or wireless network interfaces 504, one or more input / output interfaces 505, one or more keyboards 506, etc.

[0090] In one specific embodiment, the data verification processing device includes a memory and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the data verification processing device, and is configured to be executed by one or more processors. The one or more programs include computer-executable instructions for performing the following:

[0091] The object text of the transaction object is input into the processing model to select data services and generate service parameters, thereby obtaining data services and service parameters.

[0092] The data service's data interface is invoked using the service parameters as input parameters to obtain verification data.

[0093] The object text and the verification data are input into a large language model for data verification to obtain the verification results.

[0094] This specification provides an embodiment of a computer-readable storage medium as follows:

[0095] Corresponding to the data verification processing method described above, and based on the same technical concept, one or more embodiments of this specification also provide a computer-readable storage medium.

[0096] The computer-readable storage medium provided in this embodiment is used to store computer-executable instructions, which, when executed, implement the following process:

[0097] The object text of the transaction object is input into the processing model to select data services and generate service parameters, thereby obtaining data services and service parameters.

[0098] The data service's data interface is invoked using the service parameters as input parameters to obtain verification data.

[0099] The object text and the verification data are input into a large language model for data verification to obtain the verification results.

[0100] It should be noted that the embodiments of a computer-readable storage medium described in this specification and the embodiments of a data verification processing method described in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can be referred to the implementation of the corresponding method described above, and the repeated parts will not be described again.

[0101] This specification provides an example of a computer program product as follows:

[0102] Corresponding to the data verification processing method described above, based on the same technical concept, one or more embodiments of this specification also provide a computer program product.

[0103] A computer program product includes a computer program / instructions that, when executed by a processor, perform the following steps:

[0104] The object text of the transaction object is input into the processing model to select data services and generate service parameters, thereby obtaining data services and service parameters.

[0105] The data service's data interface is invoked using the service parameters as input parameters to obtain verification data.

[0106] The object text and the verification data are input into a large language model for data verification to obtain the verification results.

[0107] It should be noted that the embodiments of a computer program product described in this specification and the embodiments of a data verification processing method described in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can be referred to the implementation of the corresponding method described above, and the repeated parts will not be described again.

[0108] The various embodiments in this specification are described in a progressive manner. For the same or similar parts between the various embodiments, please refer to each other. Each embodiment focuses on describing the differences from other embodiments. For example, the device embodiments, equipment embodiments, computer-readable storage medium embodiments, and computer program product embodiments are all similar to the method embodiments, so the descriptions are relatively simple. For reading the relevant content of the device embodiments, equipment embodiments, computer-readable storage medium embodiments, and computer program product embodiments, please refer to the description of the method embodiments.

[0109] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0110] In the 1930s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many improvements to the methodology today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that an improvement to the methodology cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0111] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0112] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0113] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, when implementing the embodiments of this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0114] Those skilled in the art will understand that one or more embodiments of this specification can be provided as a method, system, or computer program product. Therefore, one or more embodiments of this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data verification processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data verification processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0116] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data verification processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data verification processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0118] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0119] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0120] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0121] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising at least one…" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0122] One or more embodiments of this specification can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a particular task or implement a particular abstract data type. One or more embodiments of this specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0123] The above description is merely an embodiment of this document and is not intended to limit the scope of this document. Various modifications and variations can be made to this document by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this document should be included within the scope of the claims of this document.

Claims

1. A data verification processing method, applied to a data verification system, the method comprising: The transaction keywords of the tradable resources are input into a pre-trained service decision model. Data services are selected from the data service list provided by the trading platform to obtain a data acquisition service. The transaction keywords are input into the parameter generation model corresponding to the data acquisition service to generate service parameters and obtain call parameters. The data acquisition service is used to obtain transaction-related data of the tradable resources. The data acquisition service's data interface is invoked using the aforementioned call parameters as interface input parameters to acquire the transaction-related data. The transaction keywords and transaction-related data are input into a large language model for data verification to obtain the verification results.

2. The data verification processing method according to claim 1, wherein the data service selection includes: The transaction keywords are segmented to obtain text keywords; The text keywords are matched with each data service in the data service list to obtain the data acquisition service.

3. The data verification processing method according to claim 1, wherein the calling parameters include at least one of the following: object name, object attribute, and time parameter.

4. The data verification processing method according to claim 1, before the steps of inputting the transaction keywords of tradable resources into a pre-trained service decision model, selecting a data service from the data service list provided by the trading platform to obtain a data acquisition service, and inputting the transaction keywords into the parameter generation model corresponding to the data acquisition service to generate service parameters and obtain calling parameters, further includes: The transaction keywords are processed according to the text processing rules of the tradable resources; The text processing rules include noise reduction rules and / or text standardization rules.

5. The data verification and processing method according to claim 1, wherein the data acquisition service is provided by a third-party platform; The data acquisition service includes: A data acquisition service for obtaining transaction-related data of the tradable resources.

6. The data verification processing method according to claim 1, wherein inputting the transaction keywords and the transaction-related data into a large language model for data verification to obtain verification results includes: Generate task text containing verification task information, the transaction keywords, and the transaction-related data; The task text is input into the large language model to verify the transaction keywords based on the transaction-related data, and the verification result is obtained.

7. A data verification processing device, operating in a data verification system, the device comprising: The service determination module is configured to input the transaction keywords of the tradable resources into a pre-trained service decision model, select a data service from the data service list provided by the trading platform to obtain a data acquisition service, and input the transaction keywords into the parameter generation model corresponding to the data acquisition service to generate service parameters to obtain call parameters; the data acquisition service is used to obtain transaction-related data of the tradable resources; The service invocation module is configured to invoke the data interface of the data acquisition service using the invocation parameters as interface input parameters to acquire the transaction-related data. The data verification module inputs the transaction keywords and transaction-related data into a large language model for data verification and obtains the verification results.

8. A data verification and processing device, comprising: processor; And, a memory configured to store computer-executable instructions, which, when executed, cause the processor to: The transaction keywords of the tradable resources are input into a pre-trained service decision model. Data services are selected from the data service list provided by the trading platform to obtain a data acquisition service. The transaction keywords are input into the parameter generation model corresponding to the data acquisition service to generate service parameters and obtain call parameters. The data acquisition service is used to obtain transaction-related data of the tradable resources. The data acquisition service's data interface is invoked using the aforementioned call parameters as interface input parameters to acquire the transaction-related data. The transaction keywords and transaction-related data are input into a large language model for data verification to obtain the verification results.

9. A computer-readable storage medium for storing computer-executable instructions that, when executed, implement the steps of the method of claim 1.

Images