Data processing method and device, equipment and storage medium

Abstract

The invention provides a data processing method and device, equipment and a storage medium, and belongs to the technical field of engineering projects. The method comprises the following steps: acquiring business related data from a business system, and acquiring financial related data from a financial system; based on the business related data and the financial related data, establishing an order basic table and an associated data table; based on the order basic table and the associated data table, determining a money return risk score corresponding to each project order; and when the money return risk score corresponding to the project order is greater than or equal to a preset risk threshold value, sending alarm prompt information corresponding to the project order to the target equipment. By adopting the method and the device, the overall fund management and control of an enterprise are facilitated, and the development efficiency of an engineering project is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of engineering projects, and in particular to a data processing method, device, equipment and storage medium. Background Art

[0002] Fund control of engineering projects is a key link in engineering project management. The effectiveness of fund control is directly related to the effect of engineering project operation.

[0003] Currently, the financial management and control of engineering projects usually requires the person in charge to apply for business-related data of each project order in the project from the business system, and apply for financial-related data of each project order in the project from the financial system. Then, based on the business-related data and financial-related data, as well as the actual situation of the project, the person in charge conducts judgments such as risk assessment of fund collection, and then conducts financial management and control of the engineering project based on the information such as the risk assessment of fund collection.

[0004] However, the above-mentioned fund control method mainly relies on manpower, which is time-consuming and laborious in obtaining business-related data and financial-related data, and mainly relies on the subjective judgment of the person in charge when conducting risk assessment of fund collection, and the results obtained by different persons in charge will also vary greatly. For enterprises with more engineering projects, this fund control method is time-consuming, labor-intensive, and highly subjective, which is not conducive to the overall fund control of the enterprise and also reduces the efficiency of the project. Summary of the invention

[0005] The disclosed embodiments provide a data processing method, which can solve the problem that the fund control method in the prior art is time-consuming, labor-intensive, highly subjective, not conducive to the overall fund management of the enterprise, and reduces the efficiency of project implementation.

[0006] In a first aspect, a data processing method is provided, the method comprising:

[0007] Acquire business-related data from a business system, and acquire financial-related data from a financial system, wherein the business-related data includes business data corresponding to a plurality of project orders, and the financial-related data includes financial data corresponding to the plurality of project orders;

[0008] Based on the business-related data and the financial-related data, an order base table and an associated data table are established, wherein the order base table includes part of the business data and part of the financial data corresponding to the multiple project orders, and the associated data table includes an associated sub-table corresponding to each project order, and the associated sub-table corresponding to the project order includes the remaining part of the business data and the remaining part of the financial data corresponding to the project order;

[0009] Determine the payment collection risk score corresponding to each project order based on the order basic table and the associated data table;

[0010] When the collection risk score corresponding to the project order is greater than or equal to a preset risk threshold, an alarm prompt message corresponding to the project order is sent to the target device.

[0011] In a possible implementation, establishing an order base table and an associated data table based on the business-related data and the financial-related data includes:

[0012] Acquire a first field value of a first target field corresponding to each project order from the business-related data, and acquire a second field value of a second target field corresponding to each project order from the finance-related data;

[0013] Based on a preset processing rule, data processing is performed on the first target field, the first field value, the second target field, and the second field value to obtain a processed first target field, a processed first field value, a processed second target field, and a processed second field value;

[0014] The order base table and the associated data table are established based on the processed first target field, the processed first field value, the processed second target field and the processed second field value.

[0015] In a possible implementation, the preset processing rules include at least one of field name standard mapping processing, field value data type standard processing, and abnormal data processing.

[0016] In a possible implementation, after determining the payment collection risk score corresponding to each project order based on the order base table and the associated data table, the method further includes:

[0017] Acquire the third field value of the third target field corresponding to each project order from the order base table and the associated data table;

[0018] A payment collection status table is established based on the third field value of the third target field and the payment collection risk score corresponding to each project order.

[0019] In one possible implementation, the collection status table includes the abbreviation of the business segment, the abbreviation of the regional company, the customer number, the customer name, the number of bad debt provisions, the balance, the aging amount of 0-1 years, the aging amount of 1-2 years, the aging amount of 2-3 years, the aging amount of 3-4 years, the aging amount of 4-5 years, the aging amount of more than 5 years, the total bad debt amount, and the collection risk score corresponding to each project order.

[0020] In a possible implementation, the order base table includes the bad debt situation corresponding to each project order, and the associated sub-table corresponding to the project order includes the aging situation corresponding to the project order;

[0021] The determining, based on the order base table and the associated data table, a payment collection risk score corresponding to each project order includes:

[0022] For each of the project orders, a collection risk score corresponding to the project order is determined based on the bad debt situation corresponding to the project order and the account age situation corresponding to the project order.

[0023] In a possible implementation, the bad debt situation corresponding to the project order includes the number of bad debts and the bad debt amount corresponding to the project order, and the aging situation corresponding to the project order includes the aging amount of the project order in different preset time periods;

[0024] The determining, based on the bad debt situation corresponding to the project order and the aging situation corresponding to the project order, a collection risk score corresponding to the project order includes:

[0025] Based on the number of bad debts and the amount of bad debts corresponding to the project order, and the aging amount of the project order in different preset time periods, the collection risk score corresponding to the project order is determined.

[0026] In a possible implementation, the order base table further includes the customer name corresponding to each project order, and the associated subtable corresponding to the project order further includes the credit status corresponding to the customer name;

[0027] The determining, based on the bad debt situation corresponding to the project order and the aging situation corresponding to the project order, a collection risk score corresponding to the project order includes:

[0028] Based on the bad debt situation corresponding to the project order, the account age situation corresponding to the project order, and the credit situation of the customer name corresponding to the project order, the collection risk score corresponding to the project order is determined.

[0029] In a second aspect, a data processing device is provided, the device comprising:

[0030] An acquisition module, used to acquire business-related data from a business system and acquire financial-related data from a financial system, wherein the business-related data includes business data corresponding to a plurality of project orders, and the financial-related data includes financial data corresponding to the plurality of project orders;

[0031] An establishing module, configured to establish an order base table and an associated data table based on the business-related data and the financial-related data, wherein the order base table includes part of the business data and part of the financial data corresponding to the multiple project orders, and the associated data table includes an associated sub-table corresponding to each project order, and the associated sub-table corresponding to the project order includes the remaining part of the business data and the remaining part of the financial data corresponding to the project order;

[0032] A risk assessment module, used to determine the collection risk score corresponding to each project order based on the order basic table and the associated data table;

[0033] The alarm module is used to send an alarm prompt message corresponding to the project order to the target device when the collection risk score corresponding to the project order is greater than or equal to a preset risk threshold.

[0034] In a possible implementation, the establishing module is used to:

[0035] Acquire a first field value of a first target field corresponding to each project order from the business-related data, and acquire a second field value of a second target field corresponding to each project order from the finance-related data;

[0036] Based on a preset processing rule, data processing is performed on the first target field, the first field value, the second target field, and the second field value to obtain a processed first target field, a processed first field value, a processed second target field, and a processed second field value;

[0037] The order base table and the associated data table are established based on the processed first target field, the processed first field value, the processed second target field and the processed second field value.

[0038] In a possible implementation, the preset processing rules include at least one of field name standard mapping processing, field value data type standard processing, and abnormal data processing.

[0039] In a possible implementation, the risk assessment module is further used to:

[0040] Acquire the third field value of the third target field corresponding to each project order from the order base table and the associated data table;

[0041] A payment collection status table is established based on the third field value of the third target field and the payment collection risk score corresponding to each project order.

[0042] In one possible implementation, the collection status table includes the abbreviation of the business segment, the abbreviation of the regional company, the customer number, the customer name, the number of bad debt provisions, the balance, the aging amount of 0-1 years, the aging amount of 1-2 years, the aging amount of 2-3 years, the aging amount of 3-4 years, the aging amount of 4-5 years, the aging amount of more than 5 years, the total bad debt amount, and the collection risk score corresponding to each project order.

[0043] In a possible implementation, the order base table includes the bad debt situation corresponding to each project order, and the associated sub-table corresponding to the project order includes the aging situation corresponding to the project order;

[0044] The risk assessment module is used to:

[0045] For each of the project orders, a collection risk score corresponding to the project order is determined based on the bad debt situation corresponding to the project order and the account age situation corresponding to the project order.

[0046] In a possible implementation, the bad debt situation corresponding to the project order includes the number of bad debts and the bad debt amount corresponding to the project order, and the aging situation corresponding to the project order includes the aging amount of the project order in different preset time periods;

[0047] The risk assessment module is used to:

[0048] Based on the number of bad debts and the amount of bad debts corresponding to the project order, and the aging amount of the project order in different preset time periods, the collection risk score corresponding to the project order is determined.

[0049] In a possible implementation, the order base table further includes the customer name corresponding to each project order, and the associated subtable corresponding to the project order further includes the credit status corresponding to the customer name;

[0050] The risk assessment module is used to:

[0051] Based on the bad debt situation corresponding to the project order, the account age situation corresponding to the project order, and the credit situation of the customer name corresponding to the project order, the collection risk score corresponding to the project order is determined.

[0052] In a third aspect, a computer device is provided, comprising a processor and a memory, wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the operation performed by the data processing method.

[0053] In a fourth aspect, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the instruction is loaded and executed by a processor to implement the operation performed by the data processing method.

[0054] In a fifth aspect, a computer program product is provided, wherein the computer program product comprises at least one instruction, and the at least one instruction is loaded and executed by a processor to implement the operation performed by the data processing method.

[0055] The beneficial effects of the technical solution provided by the disclosed embodiment are as follows: the solution mentioned in the disclosed embodiment can merge business-related data and financial-related data to obtain an order base table and an associated data table that include both business data and financial data, and the method does not require the person in charge to subjectively judge the collection risk of the project order, but can determine the collection risk score corresponding to each project order based on the data in the order base table and the associated data table. When the collection risk score is greater than or equal to the preset risk threshold, an alarm prompt message corresponding to the project order will be sent to the target system. In this way, people can directly view the business data and financial data of the project order from the order base table and the associated data table at the same time, saving time and effort, and the collection risk score obtained by a standard method improves the accuracy of the collection risk score, is more conducive to the overall capital management of the enterprise, and improves the efficiency of the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0057] Figure 1 is a flowchart of a data processing method provided by an embodiment of the present disclosure;

[0058] Figure 2 is a flowchart of a data processing method provided by an embodiment of the present disclosure;

[0059] Figure 3 It is a flow chart of establishing an order basic table and an associated data table provided by an embodiment of the present disclosure;

[0060] Figure 4 is a structural schematic diagram of a data processing device provided by an embodiment of the present disclosure;

[0061] Figure 5 is a structural block diagram of a terminal provided by an embodiment of the present disclosure;

[0062] Figure 6 It is a structural block diagram of a server provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0063] In order to make the objectives, technical solutions and advantages of the present disclosure more clear, the embodiments of the present disclosure will be further described in detail below with reference to the accompanying drawings.

[0064] The embodiments of the present disclosure provide a data processing method, which can be implemented by a computer device. The computer device can be a terminal and a server, etc. The terminal can be a desktop computer, a notebook computer, a tablet computer, a mobile phone, etc. The server can be a single server or a server cluster, etc.

[0065] A computer device may include a processor, memory, communication components, etc.

[0066] The processor can be a central processing unit (CPU), which can be used to read instructions and process data, for example, to establish an order base table and an associated data table based on business-related data and financial-related data, to determine the collection risk score corresponding to each project order based on the order base table and the associated data table, to determine whether the collection risk score corresponding to the project order is greater than or equal to a preset risk threshold, and so on.

[0067] The memory can be various volatile memories or non-volatile memories, such as solid state disks (SSDs), dynamic random access memory (DRAM), etc. The memory can be used for data storage, for example, the storage of business-related data obtained from the business system, the storage of financial-related data obtained from the financial system, the storage of established order basic tables and associated data tables, the storage of your collection risk score corresponding to each project order, and so on.

[0068] The communication component may be a wired network connector, a wireless fidelity (WiFi) module, a Bluetooth module, a cellular network communication module, etc. The communication component may be used to transmit data with other devices, for example, to obtain business-related data from a business system, to obtain financial-related data from a financial system, to send alarm information corresponding to a project order to a target system, and so on.

[0069] Figure 1 and Figure 2 is a flow chart of a data processing method provided by an embodiment of the present disclosure. Figure 1 and Figure 2 , the embodiment comprises:

[0070] 101. Obtain business-related data from the business system and obtain financial-related data from the financial system.

[0071] The business-related data includes business data corresponding to multiple project orders, and the finance-related data includes financial data corresponding to multiple project orders.

[0072] Usually, the cycle of an engineering project is long, and each engineering project usually corresponds to multiple project orders. The events and time of occurrence of these project orders may be different. Every time a new project order is added, the business data corresponding to the project order will be added to the business system, and the financial data corresponding to the project order will be added to the financial system.

[0073] In the embodiment of the present disclosure, the computer device can periodically obtain business-related data from the business system and obtain financial-related data from the financial system according to a preset period, so as to update the data in a timely manner. The preset period can be any reasonable time period, for example, the preset period can be 1 second, 5 seconds, etc., which is not limited in the embodiment of the present disclosure.

[0074] The business data corresponding to each project order may include a variety of contents, for example, the abbreviation of the business segment, the abbreviation of the regional company, the customer number, the customer name, whether bad debts are accrued, the amount of bad debts, the amount of 0-1 year aging, the amount of 1-2 years aging, the amount of 2-3 years aging, the amount of 3-4 years aging, the amount of 4-5 years aging, the amount of 5 years or more aging, etc. Of course, the business data may also include any other reasonable contents, which are not limited in the embodiments of the present disclosure.

[0075] The financial data corresponding to each project order may also include a variety of content, for example, it may include the name of the account receivable customer, the beginning amount of the account receivable, the ending amount of the account receivable, the beginning amount of the bad debt provision, the ending amount of the bad debt provision, the beginning amount of the account receivable, the ending amount of the account receivable, the beginning balance, the debit side of the current period, the credit side of the current period, the ending balance, etc. Of course, the financial data may also include any other reasonable content, which is not limited in the embodiments of the present disclosure.

[0076] 102. Based on business-related data and financial-related data, establish order basic tables and related data tables.

[0077] Among them, the order basic table includes partial business data and partial financial data corresponding to multiple project orders, the associated data table includes an associated sub-table corresponding to each project order, and the associated sub-table corresponding to the project order includes the remaining business data and the remaining financial data corresponding to the project order.

[0078] During implementation, after obtaining business-related data and financial-related data, the computer device can obtain a portion of the business data from the business data corresponding to all project orders according to the preset logic, and obtain a portion of the financial data from the financial data corresponding to all project orders, and arrange and combine this portion of business data and this portion of financial data according to the first preset arrangement rule to generate a new table, namely the order basic table.

[0079] For the remaining business data in the business data and the remaining financial data in the financial data corresponding to all project orders, an associated sub-table corresponding to each project order is generated according to the second preset arrangement rule. The associated sub-table corresponding to each project order has a data association relationship with a certain field (hereinafter referred to as the associated field) in the order basic table. When people view the order basic table, they can click on the associated field corresponding to a project order to make the computer device automatically jump to display the associated sub-table corresponding to the project order.

[0080] In the disclosed embodiment, the associated subtable corresponding to each project order may be a table or multiple tables. For example, the remaining business data and the remaining financial data corresponding to the project order may be generated into a wide table according to the second preset arrangement rule, and the wide table includes the remaining business data and the remaining financial data. Alternatively, the remaining business data and the remaining financial data corresponding to the project order may be classified and processed according to the preset classification rules to obtain multiple different types of data, and a table is generated for each type of data, thereby obtaining multiple tables. These multiple tables may have data association relationships with different associated fields in the order base table, respectively.

[0081] The embodiment of the present disclosure does not specifically limit the setting of the associated sub-table, and different settings can be made according to requirements.

[0082] In the disclosed embodiment, the order base table and the associated data table can be reasonably allocated according to needs. The more common data in the business data and the more common data in the financial data can be set in the order base table, and the remaining more detailed data and less common data can be set in the associated data table.

[0083] In this step, the data scattered in the two systems (business-related data in the business system and financial-related data in the financial system) are recombined, and the more common business data and financial data are set in the order base table, and the remaining data are set in the associated data table. In this way, people can directly view the business data and financial data corresponding to the project order in the order base table at the same time, and can view the associated sub-table of a project order through the association relationship to view the specific situation of the project order more quickly, which improves people's overall financial control over the engineering project and improves the convenience of people viewing data.

[0084] In addition, the data in the order base table and the related data table are more comprehensive. When there are other needs, for example, when it is necessary to compile statistics on customer information corresponding to each project order, data can be directly obtained from the order base table and the related data table without having to obtain data from the business system and the financial system, thereby improving people's processing efficiency.

[0085] 103. Based on the order base table and related data table, determine the collection risk score corresponding to each project order.

[0086] In implementation, the preset calculation rules can be used to perform corresponding calculations on part of the data in the order base table and the associated data table to obtain the corresponding collection risk score for each project order.

[0087] The larger the collection risk score is, the higher the collection risk of the receivables for the project order is, which will have an adverse impact on the smooth progress of the project. Conversely, the smaller the collection risk score is, the lower the collection risk of the receivables for the project order is, and the smoother the progress of the project will be.

[0088] 104. When the collection risk score corresponding to the project order is greater than or equal to the preset risk threshold, an alarm prompt message corresponding to the project order is sent to the target device.

[0089] The target device may be a work device of a person in charge of each project order.

[0090] During implementation, after determining the collection risk score corresponding to each project order, the collection risk score can be compared with the preset risk threshold. When the collection risk score corresponding to a project order is greater than or equal to the preset risk threshold, an alarm prompt message can be sent to the work device of the person in charge of the project order to remind the person in charge that the collection risk of the project order is high and needs to be monitored in time, and further collection work should be taken in advance, so as to achieve effective control of the collection risk and improve the company's overall control over project funds.

[0091] In summary, the scheme mentioned in the embodiments of the present disclosure can merge and process business-related data and financial-related data to obtain an order base table and an associated data table that include both business data and financial data. In addition, this method does not require the person in charge to subjectively judge the collection risk of the project order. Instead, the collection risk score corresponding to each project order can be determined based on the data in the order base table and the associated data table. When the collection risk score is greater than or equal to the preset risk threshold, an alarm prompt message corresponding to the project order will be sent to the target system. In this way, people can directly view the business data and financial data of the project order from the order base table and the associated data table at the same time, saving time and effort. In addition, the collection risk score obtained by a standard method improves the accuracy of the collection risk score, is more conducive to the overall capital management of the enterprise, and improves the efficiency of the project.

[0092] In the above step 102, if Figure 3 As shown, the method of establishing the order base table and the associated data table may include:

[0093] 301. Obtain a first field value of a first target field corresponding to each project order from business-related data, and obtain a second field value of a second target field corresponding to each project order from finance-related data.

[0094] The first target field and the second target field may each include multiple fields. For each project order, each corresponding first target field has a corresponding first field value, and each corresponding second target field has a corresponding second field value.

[0095] Get the first field value and the second field value corresponding to each project order for subsequent construction of the order base table and related data table.

[0096] 302. Based on preset processing rules, data processing is performed on the first target field, the first field value, the second target field, and the second field value to obtain a processed first target field, a processed first field value, a processed second target field, and a processed second field value.

[0097] In a possible implementation, the preset processing rules may include at least one of field name standard mapping processing, field value data type standard processing, and abnormal data processing.

[0098] Standard mapping processing of field names: the field names of fields in business-related data stored in the business system are more suitable for use by people who use the business system. Similarly, the field names of fields in financial-related data stored in the financial system are more suitable for use by people who use the financial system. In the present disclosed embodiment, an order base table and an associated data table that include both business data and financial data are established based on business-related data and financial-related data. In order to facilitate viewing and understanding by more people, the field names of some or all fields in the business-related data and some or all fields in the financial-related data can be changed as needed to be easier to understand. For example, the "regional company" in the business-related data can be changed to "regional company abbreviation", and so on.

[0099] In implementation, the computer device can pre-store the field names of the fields that need to be modified in the business-related data and the financial-related data and the mapping relationship between the corresponding modified field names. When establishing the order base table and the related data table, the field name of the first target field and the field name of the second target field can be changed according to the pre-stored mapping relationship to obtain the processed first target field and the processed second target field.

[0100] Field value data type standard processing: The computer device can pre-store the field type corresponding to the first target field and the field type corresponding to the second target field. After obtaining the first field value of the first target field and the field value of the second target field, the first field value that does not conform to the standard can be modified based on the pre-stored field type corresponding to the first target field to obtain the processed first field value. Similarly, the second field value that does not conform to the standard can be modified based on the pre-stored field type corresponding to the second target field to obtain the modified second field value. The processed first field value and the processed second field value are made more standardized and normalized to ensure the uniformity of the data and make the data more comparable. Among them, the field type can include text, numbers, dates, etc.

[0101] Abnormal data processing: After obtaining the first field value of the first target field and the second field value of the corresponding second target field for each project order, you can perform duplicate checking and define the data that cannot contain duplicate data as abnormal data. For example, each project order corresponds to only one unique order number. If the obtained data includes multiple duplicate order numbers, it means that it is abnormal data. At this time, you can delete these multiple duplicate order numbers and the redundant parts of the corresponding data to obtain the processed first field value and the processed second field value.

[0102] Furthermore, data that is N / A or empty in the first field value and the second field value can also be determined as abnormal data. For such data, the staff can be prompted to review and modify it, or the abnormal data can be directly deleted to obtain the processed first field value and the processed second field value.

[0103] In the embodiments of the present disclosure, the preset processing rules are not limited to the above-mentioned processing methods, and may also include any other reasonable data processing methods, which are not limited in the embodiments of the present disclosure.

[0104] 303. Create an order basic table and an associated data table based on the processed first target field, the processed first field value, the processed second target field, and the processed second field value.

[0105] In implementation, after obtaining the processed first target field, the processed first field value, the processed second target field, and the processed second field value, part of the business data and part of the financial data can be arranged and combined according to the first preset arrangement rule to obtain the order basic table, and the remaining business data and the remaining financial data can be arranged and combined according to the second preset arrangement rule to obtain the associated sub-table corresponding to each project order.

[0106] The data in the order basic table and the associated data table obtained by the above method are more normalized and standardized, which is more convenient for people to view the table and compare the data.

[0107] In one possible implementation, in step 301, after obtaining the first field value of the first target field corresponding to each project order from the business-related data, the data processing method in step 302 is used to process the first target field and the first field value, and then, based on the processed first target field and the processed first field value, a business intermediate data table is first established and stored in a computer device.

[0108] Similarly, in step 301, after obtaining the second field value of the second target field corresponding to each project order from the financial-related data, the data processing method in step 302 can be directly used to process the second target field and the second field value, and then, based on the processed second target field and the processed second field value, a financial intermediate data table is first established and stored in the computer device.

[0109] Then, based on the business intermediate data table and the financial intermediate data table, create the order basic table and related data table.

[0110] In this way, the computer device stores the business intermediate data tables and financial intermediate data tables that have undergone data processing. When people have other fund management needs in the future, they can directly obtain the business intermediate data tables and financial intermediate data tables from the computer device and process them in other ways, thereby improving people's processing efficiency.

[0111] In the above step 103, the method for determining the collection risk score corresponding to each project order may be:

[0112] The order base table includes the bad debt situation corresponding to each project order, and the associated sub-table corresponding to the project order includes the aging situation corresponding to the project order.

[0113] In this way, when determining the collection risk score corresponding to each project order, the following processing can be performed for each project order: based on the bad debt situation corresponding to the project order and the account age situation corresponding to the project order, determine the collection risk score corresponding to the project order.

[0114] In implementation, the data in the bad debt situation and the data in the aging situation corresponding to the acquired project order can be calculated by a preset algorithm, so as to obtain the collection risk score corresponding to the project order. The preset algorithm can be any reasonable algorithm, for example, the acquired bad debt situation and aging situation can be weighted and calculated based on preset weights, etc., which is not limited in the embodiments of the present disclosure.

[0115] Alternatively, the bad debt situation and the aging situation corresponding to the project order may be input into a trained collection risk prediction model to obtain a collection risk score corresponding to the project order output by the collection risk prediction model.

[0116] Among them, the trained collection risk prediction model is a trained machine learning model, which can be any reasonable machine learning network structure.

[0117] The method to obtain the trained collection risk prediction model can be:

[0118] A training sample is obtained from a training set, and the training sample may include sample input data and benchmark output data, wherein the sample input data includes the bad debt situation and aging situation corresponding to the sample project order, and the benchmark output data includes the benchmark collection risk score corresponding to the sample project order. The benchmark collection risk score may be artificially determined based on the actual collection situation of the project order. If the project order is collected on time, the benchmark collection risk score is high; if the collection of the project order is overdue or the collection time exceeds expectations, the benchmark collection risk score is low.

[0119] The sample input data is input into the collection risk prediction model to be trained to obtain the predicted collection risk score output by the collection risk prediction model to be trained.

[0120] The predicted collection risk score and the benchmark collection risk score are input into the loss function to obtain the loss value. Based on the loss value, the parameters of the collection risk prediction model to be trained are adjusted to obtain the adjusted collection risk prediction model.

[0121] Then determine whether the preset end conditions are met at the current time. If so, the adjusted collection risk prediction model will be determined as the trained collection risk prediction model. If not, continue to extract training samples from the training set and adjust the parameters of the collection risk prediction model until a trained collection risk prediction model is obtained.

[0122] In a possible implementation, the bad debt situation corresponding to the project order includes the number of bad debts and the bad debt amount corresponding to the project order, and the aging situation corresponding to the project order includes the aging amount of the project order within different preset time periods.

[0123] In this way, when determining the collection risk score corresponding to each project order, the following processing can be performed for each project order: based on the number of bad debts and the amount of bad debts corresponding to the project order, and the aging amount of the project order in different preset time periods, determine the collection risk score corresponding to the project order.

[0124] In implementation, the number of bad debts and the amount of bad debts corresponding to the project order, as well as the aging amount of the project order in different preset time periods can be calculated based on the preset algorithm, so as to obtain the collection risk score corresponding to the project order. The preset time period can be 0-1 years, 1-2 years, 2-3 years, 3-4 years, 4-5 years and more than 5 years.

[0125] The number of bad debts is positively correlated with the collection risk score, that is, the greater the number of bad debts, the greater the collection risk score when other data remain fixed.

[0126] The bad debt amount is positively correlated with the collection risk score, that is, the larger the bad debt amount, the greater the collection risk score when other data remain fixed.

[0127] The number of years and aging amount of the preset time period are positively correlated with the collection risk score. That is, when the preset time period with a longer number of years has a larger corresponding aging amount, the collection risk score will be higher when other data remain fixed.

[0128] Alternatively, the number of bad debts and the amount of bad debts corresponding to the project order, as well as the aging amount of the project order in different preset time periods, may be input into a trained collection risk prediction model to obtain the collection risk score corresponding to the project order output by the collection risk prediction model.

[0129] In a possible implementation, in addition to the bad debt situation included in the above order base table and the aging situation included in the associated sub-table, other data that affects the collection risk score can be added to make the determined collection risk score more accurate. The method can be as follows:

[0130] The order base table may further include the customer name corresponding to each project order, and the associated sub-table corresponding to the project order may further include the credit status corresponding to the customer name.

[0131] In this way, when determining the collection risk score corresponding to each project order, the following processing can be performed for each project order: based on the bad debt situation corresponding to the project order, the account age situation corresponding to the project order, and the credit situation of the customer name corresponding to the project order, determine the collection risk score corresponding to the project order.

[0132] In implementation, based on a preset algorithm, the bad debt situation corresponding to the project order, the account age situation corresponding to the project order, and the credit situation of the customer name corresponding to the project order can be calculated to obtain the collection risk score corresponding to the project order.

[0133] Alternatively, the bad debt situation corresponding to the project order, the account age situation corresponding to the project order, and the credit situation of the customer name corresponding to the project order are input into the trained collection risk prediction model to obtain the collection risk score corresponding to the project order output by the collection risk prediction model.

[0134] Among them, the credit status may include at least one of whether the business operation is abnormal, whether the person is a dishonest person, whether the payment method has been changed, whether there is a court announcement, whether the credit level has been reduced, and whether the credit limit has been reduced. Of course, it may also include any other reasonable data, which is not limited to the embodiments of the present disclosure.

[0135] It is understandable that different contents in the above credit conditions may correspond to different values. For example, as to whether the industrial and commercial operation is abnormal, if so, the corresponding value may be 1, and if not, the corresponding value may be 0.

[0136] In the disclosed embodiment, the above method can be used to determine the collection risk score corresponding to each project order in a standard determination method, without the need for the person in charge to make subjective judgments as in the prior art. In this way, the accuracy of the collection risk score is improved, and the use of a standard determination method is more conducive to the overall financial management of the enterprise and improves the efficiency of the project.

[0137] In step 103, after determining the collection risk score corresponding to each project order, the following processing may be performed:

[0138] Obtain the third field value of the third target field corresponding to each project order from the order base table and the associated data table; establish a collection status table based on the third field value of the third target field and the collection risk score corresponding to each project order.

[0139] Among them, the third target field can be relatively common data in the predetermined order basic table and related data table and / or data that has a greater impact on the collection risk score, so that people can directly view the specific factors affecting the collection risk score of the project order in the collection status table.

[0140] After obtaining the third field value of the third target field corresponding to each project order from the order base table and the associated data table, the third target field and the collection risk score can be arranged and combined according to the third preset arrangement rule to obtain a collection status table.

[0141] In one possible implementation, the collection status table may include the abbreviation of the business segment corresponding to each project order, the abbreviation of the regional company, the customer number, the customer name, the number of bad debt provisions, the balance, the 0-1 year aging amount, the 1-2 year aging amount, the 2-3 year aging amount, the 3-4 year aging amount, the 4-5 year aging amount, the 5 year and above aging amount, the total bad debt amount, and the collection risk score.

[0142] Of course, the collection status table may also include any other reasonable data, for example, it may also include the accounting period, the increase amount compared with the previous month, the increase ratio compared with the previous month, etc., and the embodiments of the present disclosure are not limited to this.

[0143] All the above optional technical solutions can be arbitrarily combined to form optional embodiments of the present disclosure, and will not be described in detail here.

[0144] The beneficial effects of the technical solution provided by the disclosed embodiment are as follows: the solution mentioned in the disclosed embodiment can merge business-related data and financial-related data to obtain an order base table and an associated data table that include both business data and financial data, and the method does not require the person in charge to subjectively judge the collection risk of the project order, but can determine the collection risk score corresponding to each project order based on the data in the order base table and the associated data table. When the collection risk score is greater than or equal to the preset risk threshold, an alarm prompt message corresponding to the project order will be sent to the target system. In this way, people can directly view the business data and financial data of the project order from the order base table and the associated data table at the same time, saving time and effort, and the collection risk score obtained by a standard method improves the accuracy of the collection risk score, is more conducive to the overall capital management of the enterprise, and improves the efficiency of the project.

[0145] The present disclosure provides a data processing device, which may be the computer device in the above embodiment. Figure 4 As shown, the device comprises:

[0146] An acquisition module 410 is used to acquire business-related data from a business system and acquire financial-related data from a financial system, wherein the business-related data includes business data corresponding to a plurality of project orders, and the financial-related data includes financial data corresponding to the plurality of project orders;

[0147] Establishing module 420, used to establish an order base table and an associated data table based on the business-related data and the financial-related data, wherein the order base table includes part of the business data and part of the financial data corresponding to the multiple project orders, and the associated data table includes an associated sub-table corresponding to each project order, and the associated sub-table corresponding to the project order includes the remaining part of the business data and the remaining part of the financial data corresponding to the project order;

[0148] A risk assessment module 430, configured to determine a payment collection risk score corresponding to each project order based on the order base table and the associated data table;

[0149] The alarm module 440 is used to send an alarm prompt message corresponding to the project order to the target device when the collection risk score corresponding to the project order is greater than or equal to a preset risk threshold.

[0150] In a possible implementation, the establishing module 420 is used to:

[0151] Acquire a first field value of a first target field corresponding to each project order from the business-related data, and acquire a second field value of a second target field corresponding to each project order from the finance-related data;

[0152] Based on a preset processing rule, data processing is performed on the first target field, the first field value, the second target field, and the second field value to obtain a processed first target field, a processed first field value, a processed second target field, and a processed second field value;

[0153] The order base table and the associated data table are established based on the processed first target field, the processed first field value, the processed second target field and the processed second field value.

[0154] In a possible implementation, the preset processing rules include at least one of field name standard mapping processing, field value data type standard processing, and abnormal data processing.

[0155] In a possible implementation, the risk assessment module 430 is further configured to:

[0156] Acquire the third field value of the third target field corresponding to each project order from the order base table and the associated data table;

[0157] A payment collection status table is established based on the third field value of the third target field and the payment collection risk score corresponding to each project order.

[0158] In one possible implementation, the collection status table includes the abbreviation of the business segment, the abbreviation of the regional company, the customer number, the customer name, the number of bad debt provisions, the balance, the aging amount of 0-1 years, the aging amount of 1-2 years, the aging amount of 2-3 years, the aging amount of 3-4 years, the aging amount of 4-5 years, the aging amount of more than 5 years, the total bad debt amount, and the collection risk score corresponding to each project order.

[0159] In a possible implementation, the order base table includes the bad debt situation corresponding to each project order, and the associated sub-table corresponding to the project order includes the aging situation corresponding to the project order;

[0160] The risk assessment module 430 is used to:

[0161] For each of the project orders, a collection risk score corresponding to the project order is determined based on the bad debt situation corresponding to the project order and the account age situation corresponding to the project order.

[0162] In a possible implementation, the bad debt situation corresponding to the project order includes the number of bad debts and the bad debt amount corresponding to the project order, and the aging situation corresponding to the project order includes the aging amount of the project order in different preset time periods;

[0163] The risk assessment module 430 is used to:

[0164] Based on the number of bad debts and the amount of bad debts corresponding to the project order, and the aging amount of the project order in different preset time periods, the collection risk score corresponding to the project order is determined.

[0165] In a possible implementation, the order base table further includes the customer name corresponding to each project order, and the associated subtable corresponding to the project order further includes the credit status corresponding to the customer name;

[0166] The risk assessment module 430 is used to:

[0167] Based on the bad debt situation corresponding to the project order, the account age situation corresponding to the project order, and the credit situation of the customer name corresponding to the project order, the collection risk score corresponding to the project order is determined.

[0168] It should be noted that: when the data processing device provided in the above embodiment performs data processing, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the data processing device provided in the above embodiment and the data processing method embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0169] Figure 5 The structural block diagram of a terminal 500 provided by an exemplary embodiment of the present disclosure is shown. The terminal may be the computer device in the above-mentioned embodiment. The terminal 500 may be: a smart phone, a tablet computer, an MP3 player (moving picture experts group audio layer III, dynamic image expert compression standard audio layer 3), an MP4 (moving picture experts group audio layer IV, dynamic image expert compression standard audio layer 4) player, a laptop computer or a desktop computer. The terminal 500 may also be called a user device, a portable terminal, a laptop terminal, a desktop terminal or other names.

[0170] Typically, the terminal 500 includes a processor 501 and a memory 502 .

[0171] The processor 501 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 501 may be implemented in at least one hardware form of DSP (digital signal processing), FPGA (field-programmable gate array), and PLA (programmable logic array). The processor 501 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (central processing unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 501 may be integrated with a GPU (graphics processing unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 501 may also include an AI (artificial intelligence) processor, which is used to process computing operations related to machine learning.

[0172] The memory 502 may include one or more computer-readable storage media, which may be non-transitory. The memory 502 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 502 is used to store at least one instruction, which is used to be executed by the processor 501 to implement the data processing method provided by the method embodiment of the present disclosure.

[0173] In some embodiments, the terminal 500 may further optionally include: a peripheral device interface 503 and at least one peripheral device. The processor 501, the memory 502 and the peripheral device interface 503 may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface 503 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 504, a display screen 505, a camera 506, an audio circuit 507, a positioning component 508 and a power supply 509.

[0174] The peripheral device interface 503 may be used to connect at least one peripheral device related to I / O (input / output) to the processor 501 and the memory 502. In some embodiments, the processor 501, the memory 502, and the peripheral device interface 503 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 501, the memory 502, and the peripheral device interface 503 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0175] The radio frequency circuit 504 is used to receive and transmit RF (radio frequency) signals, also known as electromagnetic signals. The radio frequency circuit 504 communicates with the communication network and other communication devices through electromagnetic signals. The radio frequency circuit 504 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the radio frequency circuit 504 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The radio frequency circuit 504 can communicate with other terminals through at least one wireless communication protocol. The wireless communication protocol includes but is not limited to: a metropolitan area network, various generations of mobile communication networks (2G, 3G, 4G and 5G), a wireless local area network and / or a WiFi (wireless fidelity) network. In some embodiments, the radio frequency circuit 504 may also include circuits related to NFC (near field communication), which is not limited in the present disclosure.

[0176] The display screen 505 is used to display a UI (user interface). The UI may include graphics, text, icons, videos, and any combination thereof. When the display screen 505 is a touch display screen, the display screen 505 also has the ability to collect touch signals on the surface or above the surface of the display screen 505. The touch signal can be input to the processor 501 as a control signal for processing. At this time, the display screen 505 can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, the display screen 505 can be one, and the front panel of the terminal 500 is set; in other embodiments, the display screen 505 can be at least two, which are respectively set on different surfaces of the terminal 500 or are folded; in some other embodiments, the display screen 505 can be a flexible display screen, which is set on the curved surface or folded surface of the terminal 500. Even, the display screen 505 can also be set as a non-rectangular irregular shape, that is, a special-shaped screen. The display screen 505 can be made of materials such as LCD (liquid crystal display) and OLED (organic light-emitting diode).

[0177] The camera assembly 506 is used to capture images or videos. Optionally, the camera assembly 506 includes a front camera and a rear camera. Typically, the front camera is arranged on the front panel of the terminal, and the rear camera is arranged on the back of the terminal. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth of field camera, a wide-angle camera, and a telephoto camera, so as to realize the fusion of the main camera and the depth of field camera to realize the background blur function, the fusion of the main camera and the wide-angle camera to realize panoramic shooting and VR (virtual reality) shooting function or other fusion shooting functions. In some embodiments, the camera assembly 506 may also include a flash. The flash can be a monochrome temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation at different color temperatures.

[0178] The audio circuit 507 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals and input them into the processor 501 for processing, or input them into the radio frequency circuit 504 to achieve voice communication. For the purpose of stereo acquisition or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the terminal 500. The microphone may also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert the electrical signal from the processor 501 or the radio frequency circuit 504 into sound waves. The speaker may be a traditional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into sound waves audible to humans, but also convert the electrical signal into sound waves inaudible to humans for purposes such as ranging. In some embodiments, the audio circuit 507 may also include a headphone jack.

[0179] The positioning component 508 is used to locate the current geographical location of the terminal 500 to implement navigation or LBS (location based service). The positioning component 508 can be a positioning component based on GPS (global positioning system), Beidou system, Greiner system or Galileo system.

[0180] The power supply 509 is used to power various components in the terminal 500. The power supply 509 can be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 509 includes a rechargeable battery, the rechargeable battery can support wired charging or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0181] In some embodiments, the terminal 500 further includes one or more sensors 510 , including but not limited to: an acceleration sensor 511 , a gyroscope sensor 512 , a pressure sensor 513 , a fingerprint sensor 514 , an optical sensor 515 , and a proximity sensor 516 .

[0182] The acceleration sensor 511 can detect the magnitude of acceleration on the three coordinate axes of the coordinate system established by the terminal 500. For example, the acceleration sensor 511 can be used to detect the components of gravity acceleration on the three coordinate axes. The processor 501 can control the display screen 505 to display the user interface in a horizontal view or a vertical view according to the gravity acceleration signal collected by the acceleration sensor 511. The acceleration sensor 511 can also be used to collect game or user motion data.

[0183] The gyro sensor 512 can detect the body direction and rotation angle of the terminal 500, and the gyro sensor 512 can cooperate with the acceleration sensor 511 to collect the user's 3D actions on the terminal 500. The processor 501 can implement the following functions based on the data collected by the gyro sensor 512: motion sensing (such as changing the UI according to the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.

[0184] The pressure sensor 513 can be set on the side frame of the terminal 500 and / or the lower layer of the display screen 505. When the pressure sensor 513 is set on the side frame of the terminal 500, it can detect the user's holding signal of the terminal 500, and the processor 501 performs left and right hand recognition or shortcut operation according to the holding signal collected by the pressure sensor 513. When the pressure sensor 513 is set on the lower layer of the display screen 505, the processor 501 controls the operability controls on the UI interface according to the user's pressure operation on the display screen 505. The operability controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0185] The fingerprint sensor 514 is used to collect the user's fingerprint, and the processor 501 identifies the user's identity based on the fingerprint collected by the fingerprint sensor 514, or the fingerprint sensor 514 identifies the user's identity based on the collected fingerprint. When the user's identity is identified as a trusted identity, the processor 501 authorizes the user to perform relevant sensitive operations, which include unlocking the screen, viewing encrypted information, downloading software, paying, and changing settings. The fingerprint sensor 514 can be set on the front, back, or side of the terminal 500. When a physical button or a manufacturer logo is set on the terminal 500, the fingerprint sensor 514 can be integrated with the physical button or the manufacturer logo.

[0186] The optical sensor 515 is used to collect the ambient light intensity. In one embodiment, the processor 501 can control the display brightness of the display screen 505 according to the ambient light intensity collected by the optical sensor 515. Specifically, when the ambient light intensity is high, the display brightness of the display screen 505 is increased; when the ambient light intensity is low, the display brightness of the display screen 505 is reduced. In another embodiment, the processor 501 can also dynamically adjust the shooting parameters of the camera component 506 according to the ambient light intensity collected by the optical sensor 515.

[0187] The proximity sensor 516, also called a distance sensor, is usually disposed on the front panel of the terminal 500. The proximity sensor 516 is used to collect the distance between the user and the front of the terminal 500. In one embodiment, when the proximity sensor 516 detects that the distance between the user and the front of the terminal 500 is gradually decreasing, the processor 501 controls the display screen 505 to switch from the screen-on state to the screen-off state; when the proximity sensor 516 detects that the distance between the user and the front of the terminal 500 is gradually increasing, the processor 501 controls the display screen 505 to switch from the screen-off state to the screen-on state.

[0188] Those skilled in the art will understand that Figure 5 The structure shown in the figure does not constitute a limitation on the terminal 500, and the terminal 500 may include more or less components than those shown in the figure, or combine some components, or adopt a different component arrangement.

[0189] Figure 6 6 is a schematic diagram of the structure of a server provided by an embodiment of the present disclosure. The server 600 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 601 and one or more memories 602, wherein the memory 602 stores at least one instruction, and the at least one instruction is loaded and executed by the processor 601 to implement the methods provided by the above-mentioned various method embodiments. Of course, the server may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output, and the server may also include other components for implementing device functions, which will not be described in detail here.

[0190] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including instructions, and the instructions can be executed by a processor in a terminal to complete the data processing method in the above embodiment. The computer-readable storage medium can be non-transitory. For example, the computer-readable storage medium can be a ROM (read-only memory), a RAM (random access memory), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device.

[0191] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0192] The terms "first", "second", "third" and "fourth" etc. in the specification and claims of the present application and the drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0193] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals (including but not limited to signals transmitted between user terminals and other devices, etc.) involved in this disclosure are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions. For example, the "business-related data" and "financial-related data" involved in this disclosure are all obtained with full authorization.

[0194] The above description is only an optional embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A data processing method, characterized in that: The method comprises: Acquire business-related data from a business system, and acquire financial-related data from a financial system, wherein the business-related data includes business data corresponding to a plurality of project orders, and the financial-related data includes financial data corresponding to the plurality of project orders; Based on the business-related data and the financial-related data, an order base table and an associated data table are established, wherein the order base table includes part of the business data and part of the financial data corresponding to the multiple project orders, and the associated data table includes an associated sub-table corresponding to each project order, and the associated sub-table corresponding to the project order includes the remaining part of the business data and the remaining part of the financial data corresponding to the project order; Determine the payment collection risk score corresponding to each project order based on the order basic table and the associated data table; When the collection risk score corresponding to the project order is greater than or equal to a preset risk threshold, an alarm prompt message corresponding to the project order is sent to the target device.

2. The method according to claim 1, characterized in that: The step of establishing an order base table and an associated data table based on the business-related data and the financial-related data includes: Acquire a first field value of a first target field corresponding to each project order from the business-related data, and acquire a second field value of a second target field corresponding to each project order from the finance-related data; Based on a preset processing rule, data processing is performed on the first target field, the first field value, the second target field, and the second field value to obtain a processed first target field, a processed first field value, a processed second target field, and a processed second field value; The order base table and the associated data table are established based on the processed first target field, the processed first field value, the processed second target field and the processed second field value.

3. The method according to claim 2, characterized in that The preset processing rules include at least one of field name standard mapping processing, field value data type standard processing, and abnormal data processing.

4. The method according to claim 1, characterized in that: After determining the payment collection risk score corresponding to each project order based on the order basic table and the associated data table, the method further includes: Acquire the third field value of the third target field corresponding to each project order from the order base table and the associated data table; A payment collection status table is established based on the third field value of the third target field and the payment collection risk score corresponding to each project order.

5. The method according to claim 4, characterized in that The collection status table includes the abbreviation of the business segment, the abbreviation of the regional company, the customer number, the customer name, the number of bad debt provisions, the balance, the aging amount of 0-1 years, the aging amount of 1-2 years, the aging amount of 2-3 years, the aging amount of 3-4 years, the aging amount of 4-5 years, the aging amount of more than 5 years, the total bad debt amount, and the collection risk score corresponding to each project order.

6. The method according to claim 1, characterized in that The order base table includes the bad debt situation corresponding to each project order, and the associated sub-table corresponding to the project order includes the aging situation corresponding to the project order; The determining, based on the order base table and the associated data table, a payment collection risk score corresponding to each project order includes: For each of the project orders, a collection risk score corresponding to the project order is determined based on the bad debt situation corresponding to the project order and the account age situation corresponding to the project order.

7. The method according to claim 6, characterized in that The bad debt situation corresponding to the project order includes the number of bad debts and the bad debt amount corresponding to the project order, and the aging situation corresponding to the project order includes the aging amount of the project order in different preset time periods; The determining, based on the bad debt situation corresponding to the project order and the aging situation corresponding to the project order, a collection risk score corresponding to the project order includes: Based on the number of bad debts and the amount of bad debts corresponding to the project order, and the aging amount of the project order in different preset time periods, the collection risk score corresponding to the project order is determined.

8. The method according to claim 6, characterized in that The order base table also includes the customer name corresponding to each project order, and the associated sub-table corresponding to the project order also includes the credit status corresponding to the customer name; The determining, based on the bad debt situation corresponding to the project order and the aging situation corresponding to the project order, a collection risk score corresponding to the project order includes: Based on the bad debt situation corresponding to the project order, the account age situation corresponding to the project order, and the credit situation of the customer name corresponding to the project order, the collection risk score corresponding to the project order is determined.

9. A data processing device, characterized in that: The device comprises: An acquisition module, used to acquire business-related data from a business system and acquire financial-related data from a financial system, wherein the business-related data includes business data corresponding to a plurality of project orders, and the financial-related data includes financial data corresponding to the plurality of project orders; An establishing module, configured to establish an order base table and an associated data table based on the business-related data and the financial-related data, wherein the order base table includes part of the business data and part of the financial data corresponding to the multiple project orders, and the associated data table includes an associated sub-table corresponding to each project order, and the associated sub-table corresponding to the project order includes the remaining part of the business data and the remaining part of the financial data corresponding to the project order; A risk assessment module, used to determine the collection risk score corresponding to each project order based on the order basic table and the associated data table; The alarm module is used to send an alarm prompt message corresponding to the project order to the target device when the collection risk score corresponding to the project order is greater than or equal to a preset risk threshold.

10. The device according to claim 9, characterized in that The establishment module is used to: Acquire a first field value of a first target field corresponding to each project order from the business-related data, and acquire a second field value of a second target field corresponding to each project order from the finance-related data; Based on a preset processing rule, data processing is performed on the first target field, the first field value, the second target field, and the second field value to obtain a processed first target field, a processed first field value, a processed second target field, and a processed second field value; The order base table and the associated data table are established based on the processed first target field, the processed first field value, the processed second target field and the processed second field value.

11. The device according to claim 10, characterized in that The preset processing rules include at least one of field name standard mapping processing, field value data type standard processing, and abnormal data processing.

12. The device according to claim 9, characterized in that The risk assessment module is also used to: Acquire the third field value of the third target field corresponding to each project order from the order base table and the associated data table; A payment collection status table is established based on the third field value of the third target field and the payment collection risk score corresponding to each project order.

13. The device according to claim 12, characterized in that The collection status table includes the abbreviation of the business segment, the abbreviation of the regional company, the customer number, the customer name, the number of bad debt provisions, the balance, the aging amount of 0-1 years, the aging amount of 1-2 years, the aging amount of 2-3 years, the aging amount of 3-4 years, the aging amount of 4-5 years, the aging amount of more than 5 years, the total bad debt amount, and the collection risk score corresponding to each project order.

14. The device according to claim 9, characterized in that The order base table includes the bad debt situation corresponding to each project order, and the associated sub-table corresponding to the project order includes the aging situation corresponding to the project order; The risk assessment module is used to: For each of the project orders, a collection risk score corresponding to the project order is determined based on the bad debt situation corresponding to the project order and the account age situation corresponding to the project order.

15. The device according to claim 14, characterized in that The bad debt situation corresponding to the project order includes the number of bad debts and the bad debt amount corresponding to the project order, and the aging situation corresponding to the project order includes the aging amount of the project order in different preset time periods; The risk assessment module is used to: Based on the number of bad debts and the amount of bad debts corresponding to the project order, and the aging amount of the project order in different preset time periods, the collection risk score corresponding to the project order is determined.

16. The device according to claim 14, characterized in that The order base table also includes the customer name corresponding to each project order, and the associated sub-table corresponding to the project order also includes the credit status corresponding to the customer name; The risk assessment module is used to: Based on the bad debt situation corresponding to the project order, the account age situation corresponding to the project order, and the credit situation of the customer name corresponding to the project order, the collection risk score corresponding to the project order is determined.

17. A computer device, characterized in that: The computer device includes a processor and a memory, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the operation performed by the data processing method according to any one of claims 1 to 8.

18. A computer-readable storage medium, characterized in that: The storage medium stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the operation performed by the data processing method according to any one of claims 1 to 8.

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