Data processing method and system for project bid invitation management

Abstract

The invention discloses a data processing method and system for engineering bid invitation management. The method comprises the steps of obtaining a bid invitation planning request sent by bid invitation planning equipment for a target engineering project; the bid invitation planning request is sent to at least one piece of approval terminal equipment according to a preset approval rule; generating a bid inviting scheme corresponding to the target engineering project according to the bid inviting planning request and an approval result of the approval end equipment; obtaining a bid invitation management application sent by a management device, and determining a bid invitation execution scheme of the bid invitation management application according to the bid invitation management application and a corresponding approval rule; and obtaining a supplier management application sent by a management device, and determining a management execution scheme of the supplier management application according to the supplier management application and the corresponding supplier historical data. Therefore, comprehensive project bid invitation management can be realized in combination with a data processing technology, the efficiency and effect of project bid invitation management are improved, and help is provided for smooth development of the project.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a data processing method and system for engineering bidding management. Background Art

[0002] As the scale of construction projects increases day by day, the demand for project bidding is also increasing. How to ensure the smoothness and accuracy of the bidding process while implementing a large number of project bidding is an important issue. Although the existing technology partially combines data processing technology to achieve some bidding management, it does not make full use of approval rules and supplier historical data to improve the efficiency of bidding management. It can be seen that the existing technology has defects and needs to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a data processing method and system for engineering bidding management, which can combine data processing technology to realize comprehensive engineering bidding management, improve the efficiency and effect of engineering bidding management, and provide assistance for the smooth development of the project.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a data processing method for project bidding management, the method comprising: Obtaining a bidding planning request for a target engineering project sent by a bidding planning device; the bidding planning request is used to be sent to at least one approval end device according to a preset approval rule; Generate a bidding plan corresponding to the target engineering project according to the bidding planning request and the approval result of the approval terminal device; Obtaining a tender management application sent by a management device, and determining a tender execution plan for the tender management application according to the tender management application and corresponding approval rules; A supplier management application sent by a management device is obtained, and a management execution plan for the supplier management application is determined according to the supplier management application and corresponding supplier historical data.

[0005] As an optional implementation, in the first aspect of the present invention, the bidding planning request includes the project name, planning person in charge, planning compiler, planning date requirements, planning approval requirements, bidding needs and bidding scheme; the bidding needs include the bidding project, bidding subject, section name, section scope, bidding classification and bidding responsible person.

[0006] As an optional embodiment, in the first aspect of the present invention, the bidding management application includes at least one of a new bidding application, a bidding document preparation application, a qualification review application, a bidding document processing application, a bid opening and evaluation application, a winning bid notification application and a document archiving application.

[0007] As an optional embodiment, in the first aspect of the present invention, the supplier management application includes at least one of an application for adding a new supplier, an application for inspecting a supplier, an application for approving a supplier, an application for admitting a supplier to the warehouse, an application for viewing supplier information, an application for modifying supplier information, and an application for blacklisting a supplier.

[0008] As an optional implementation, in the first aspect of the present invention, determining the management execution plan of the supplier management application according to the supplier management application and the corresponding supplier historical data includes: When the supplier management application is a supplier entry application, historical approval data, historical submission documents, historical credit data and historical service object data of the supplier object corresponding to the supplier entry application are obtained; Determine the reliability parameter and the adaptability parameter of the supplier object based on the trained neural network model and the historical approval data, the historical submission documents, the historical credit data and the historical service object data; Determine the fraud degree parameter corresponding to the supplier object according to the reliability parameter and the adaptation degree parameter, as well as the information of the remaining unselected suppliers corresponding to the supplier entry application; The warehousing result corresponding to the supplier object is determined according to the reliability parameter, the adaptation parameter and the fraud degree parameter.

[0009] As an optional implementation, in the first aspect of the present invention, the reliability parameter and the adaptability parameter of the supplier object are determined based on the trained neural network model, the historical approval data, the historical submission documents, the historical credit data and the historical service object data, including: Inputting the historical submission documents and the historical credit data into a trained reliable prediction neural network to obtain a reliability parameter corresponding to the supplier object; the reliable prediction neural network is trained by a training data set including submission document data and credit data of multiple training suppliers and corresponding reliability annotations; The historical approval data and the historical service object data are input into a trained adaptation prediction neural network to obtain the adaptation degree parameter corresponding to the supplier object; the adaptation prediction neural network is trained by a training data set including approval data and service object data of multiple training suppliers and corresponding adaptability annotations.

[0010] As an optional implementation, in the first aspect of the present invention, determining the fraud degree parameter corresponding to the supplier object according to the reliability parameter and the adaptation degree parameter, and the information of the remaining unselected suppliers corresponding to the supplier entry application, includes: Obtaining the remaining multiple unsuccessful suppliers corresponding to the supplier's entry application; Determining the reliability parameter and the suitability parameter of each of the unsuccessful suppliers; Calculate the average value of the parameter distance between the reliability parameter corresponding to the supplier object and the reliability parameters of all the unsuccessful suppliers to obtain a reliability gap parameter; Calculate the average value of the parameter distance between the fitness parameter corresponding to the supplier object and the fitness parameters of all the unsuccessful suppliers to obtain a fitness gap parameter; The weighted average value of the reliability gap parameter and the adaptation gap parameter is calculated to obtain a counterfeiting degree parameter corresponding to the supplier object.

[0011] As an optional implementation, in the first aspect of the present invention, determining the warehousing result corresponding to the supplier object according to the reliability parameter, the adaptation parameter and the fraud parameter includes: When the reliability parameter is greater than the reliability parameter threshold, the adaptation parameter is greater than the adaptation parameter threshold, and the fraud parameter is less than the first fraud parameter threshold, determining that the warehousing result of the supplier object is approved for warehousing; otherwise, determining that the warehousing result of the supplier object is approved for warehousing; When the fraud degree parameter is greater than a second fraud parameter threshold, the warning information corresponding to the supplier object is sent to a designated approval user terminal device.

[0012] A second aspect of an embodiment of the present invention discloses a data processing system for project bidding management, the system comprising: An acquisition module, used to acquire a tender planning request for a target engineering project sent by a tender planning device; the tender planning request is used to be sent to at least one approval end device according to a preset approval rule; An approval module, used to generate a bidding plan corresponding to the target engineering project according to the bidding planning request and the approval result of the approval terminal device; A determination module, used to obtain the tender management application sent by the management device, and determine the tender execution plan of the tender management application according to the tender management application and the corresponding approval rules; The execution module is used to obtain the supplier management application sent by the management device, and determine the management execution plan of the supplier management application according to the supplier management application and the corresponding supplier historical data.

[0013] As an optional implementation, in the second aspect of the present invention, the bidding planning request includes the project name, planning person in charge, planning compiler, planning date requirements, planning approval requirements, bidding needs and bidding scheme; the bidding needs include the bidding project, bidding subject, section name, section scope, bidding classification and bidding responsible person.

[0014] As an optional embodiment, in the second aspect of the present invention, the bidding management application includes at least one of a new bidding application, a bidding document preparation application, a qualification review application, a bidding document processing application, a bid opening and evaluation application, a winning bid notification application and a document archiving application.

[0015] As an optional embodiment, in the second aspect of the present invention, the supplier management application includes at least one of an application for adding a new supplier, an application for inspecting a supplier, an application for approving a supplier, an application for admitting a supplier to the warehouse, an application for viewing supplier information, an application for modifying supplier information, and an application for blacklisting a supplier.

[0016] As an optional implementation, in the second aspect of the present invention, the execution module determines the specific manner of the management execution plan of the supplier management application according to the supplier management application and the corresponding supplier historical data, including: When the supplier management application is a supplier entry application, historical approval data, historical submission documents, historical credit data and historical service object data of the supplier object corresponding to the supplier entry application are obtained; Determine the reliability parameter and the adaptability parameter of the supplier object based on the trained neural network model and the historical approval data, the historical submission documents, the historical credit data and the historical service object data; Determine the fraud degree parameter corresponding to the supplier object according to the reliability parameter and the adaptation degree parameter, as well as the information of the remaining unselected suppliers corresponding to the supplier entry application; The warehousing result corresponding to the supplier object is determined according to the reliability parameter, the adaptation parameter and the fraud degree parameter.

[0017] As an optional implementation, in the second aspect of the present invention, the execution module determines the reliability parameter and the adaptation parameter of the supplier object in a specific manner based on the trained neural network model, the historical approval data, the historical submission documents, the historical credit data and the historical service object data, including: Inputting the historical submission documents and the historical credit data into a trained reliable prediction neural network to obtain a reliability parameter corresponding to the supplier object; the reliable prediction neural network is trained by a training data set including submission document data and credit data of multiple training suppliers and corresponding reliability annotations; The historical approval data and the historical service object data are input into a trained adaptation prediction neural network to obtain the adaptation degree parameter corresponding to the supplier object; the adaptation prediction neural network is trained by a training data set including approval data and service object data of multiple training suppliers and corresponding adaptability annotations.

[0018] As an optional implementation, in the second aspect of the present invention, the execution module determines the specific manner in which the counterfeiting degree parameter corresponding to the supplier object is determined according to the reliability parameter and the adaptation degree parameter, and the information of the remaining unselected suppliers corresponding to the supplier entry application, including: Obtaining the remaining multiple unsuccessful suppliers corresponding to the supplier's entry application; Determining the reliability parameter and the suitability parameter of each of the unsuccessful suppliers; Calculate the average value of the parameter distance between the reliability parameter corresponding to the supplier object and the reliability parameters of all the unsuccessful suppliers to obtain a reliability gap parameter; Calculate the average value of the parameter distance between the fitness parameter corresponding to the supplier object and the fitness parameters of all the unsuccessful suppliers to obtain a fitness gap parameter; The weighted average value of the reliability gap parameter and the adaptation gap parameter is calculated to obtain a counterfeiting degree parameter corresponding to the supplier object.

[0019] As an optional implementation, in the second aspect of the present invention, the execution module determines the specific manner of the warehousing result corresponding to the supplier object according to the reliability parameter, the adaptation parameter and the fraud degree parameter, including: When the reliability parameter is greater than the reliability parameter threshold, the adaptation parameter is greater than the adaptation parameter threshold, and the fraud parameter is less than the first fraud parameter threshold, determining that the warehousing result of the supplier object is approved for warehousing; otherwise, determining that the warehousing result of the supplier object is approved for warehousing; When the fraud degree parameter is greater than a second fraud parameter threshold, the warning information corresponding to the supplier object is sent to a designated approval user terminal device.

[0020] The third aspect of the present invention discloses another data processing system for project bidding management, the system comprising: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute part or all of the steps in the data processing method for engineering bidding management disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the data processing method for engineering bidding management disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention can approve bidding planning requests or management requests through preset approval rules and approval results, and perform supplier management based on the supplier's historical data, thereby being able to combine data processing technology to achieve comprehensive project bidding management, improve the efficiency and effectiveness of project bidding management, and provide assistance for the smooth development of the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a flow chart of a data processing method for engineering bidding management disclosed in an embodiment of the present invention.

[0025] Figure 2 The invention discloses a data processing system for project bidding management.

[0026] Figure 3 It is a structural schematic diagram of another data processing system for engineering bidding management disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned 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, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0029] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] The present invention discloses a data processing method and system for project bidding management, which can approve bidding planning requests or management requests through preset approval rules and approval results, and perform supplier management based on supplier's historical data, so as to realize comprehensive project bidding management by combining data processing technology, improve the efficiency and effect of project bidding management, and provide help for the smooth development of projects. The following are detailed descriptions.

[0031] Embodiment 1 See also Figure 1 , Figure 1 This is a flow chart of a data processing method for project bidding management disclosed in an embodiment of the present invention. Figure 1 The data processing method for project bidding management described above can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the data processing method for engineering bidding management may include the following operations: 101. Obtain the bidding planning request for the target engineering project sent by the bidding planning device.

[0032] Optionally, the bidding planning request is used to be sent to at least one approval end device according to preset approval rules.

[0033] 102. Generate a bidding plan corresponding to the target project based on the bidding planning request and the approval result of the approval end equipment. 103. Obtain the tender management application sent by the management device, and determine the tender implementation plan of the tender management application based on the tender management application and corresponding approval rules. 104. Obtain the supplier management application sent by the management device, and determine the management execution plan of the supplier management application based on the supplier management application and the corresponding supplier historical data.

[0034] It can be seen that the above-mentioned embodiments of the invention can approve bidding planning requests or management requests through preset approval rules and approval results, and perform supplier management based on the supplier's historical data, thereby being able to combine data processing technology to achieve comprehensive engineering bidding management, improve the efficiency and effectiveness of engineering bidding management, and provide assistance for the smooth development of the project.

[0035] As an optional embodiment, in the above steps, the bidding planning request includes the project name, planning person in charge, planning compiler, planning date requirements, planning approval requirements, bidding requirements and bidding plan; the bidding requirements include the bidding project, bidding subject, section name, section scope, bidding classification and bidding responsible person.

[0036] It can be seen that through the above optional embodiments, the content of the bidding planning request is clarified, which can make the bidding planning more comprehensive and accurate, so as to improve the information sufficiency of the final bidding plan, thereby helping to improve the efficiency and effectiveness of project bidding management and provide assistance for the smooth progress of the project.

[0037] As an optional embodiment, in the above steps, the bidding management application includes at least one of a new bidding application, a bidding document preparation application, a qualification review application, a bidding document processing application, a bid opening and evaluation application, a winning bid notification application and a document archiving application.

[0038] It can be seen that through the above optional embodiments, the types of bidding management applications are clarified, and more comprehensive bidding management functions can be implemented, thereby assisting in improving the efficiency and effectiveness of project bidding management and providing assistance for the smooth progress of the project.

[0039] As an optional embodiment, in the above steps, the supplier management application includes at least one of an application for adding a new supplier, an application for inspecting a supplier, an application for approving a supplier, an application for admitting a supplier to the warehouse, an application for viewing supplier information, an application for modifying supplier information, and an application for blacklisting a supplier.

[0040] It can be seen that through the above optional embodiments, the types of supplier management applications are clarified, and a more comprehensive supplier management function can be implemented to effectively manage various supplier-related needs, thereby assisting in improving the efficiency and effectiveness of project bidding management and providing assistance for the smooth progress of the project.

[0041] As an optional embodiment, in the above steps, determining the management execution plan of the supplier management application according to the supplier management application and the corresponding supplier historical data includes: When the supplier management application is a supplier entry application, obtain the historical approval data, historical submission documents, historical credit data and historical service object data of the supplier object corresponding to the supplier entry application; According to the trained neural network model, based on historical approval data, historical submission documents, historical credit data and historical service object data, the reliability parameters and adaptability parameters of the supplier object are determined; Determine the fraud degree parameter corresponding to the supplier object based on the reliability parameter and the adaptability parameter, as well as the information of the remaining unselected suppliers corresponding to the supplier entry application; According to the reliability parameter, adaptation parameter and fraud degree parameter, the warehousing result corresponding to the supplier object is determined.

[0042] It can be seen that through the above-mentioned optional embodiments, the reliability, adaptability and cost can be determined based on the supplier's historical approval data, submitted documents, credit data and service objects, so as to finally determine the supplier's warehousing results and improve the accuracy of supplier screening, thereby assisting in improving the efficiency and effectiveness of project bidding management and providing assistance for the smooth progress of the project.

[0043] As an optional embodiment, in the above steps, according to the trained neural network model, based on historical approval data, historical submission documents, historical credit data and historical service object data, the reliability parameter and the adaptability parameter of the supplier object are determined, including: Inputting historical submission documents and historical credit data into a trained reliable prediction neural network to obtain a reliability parameter corresponding to the supplier object; optionally, the reliable prediction neural network is trained using a training data set including submission document data and credit data of multiple training suppliers and corresponding reliability annotations; The historical approval data and historical service object data are input into the trained adaptation prediction neural network to obtain the adaptation degree parameters corresponding to the supplier object; the adaptation prediction neural network is trained by a training data set including approval data and service object data of multiple training suppliers and corresponding adaptability annotations.

[0044] It can be seen that through the above-mentioned optional embodiments, the reliability and adaptability of the supplier can be determined respectively based on the reliable prediction neural network and the adaptive prediction neural network, so as to finally determine the supplier's warehousing results and improve the accuracy of supplier screening, thereby assisting in improving the efficiency and effectiveness of project bidding management and providing assistance for the smooth progress of the project.

[0045] As an optional embodiment, in the above steps, according to the reliability parameter and the adaptability parameter, and the information of the remaining unselected suppliers corresponding to the supplier entry application, determining the fraud degree parameter corresponding to the supplier object includes: Obtain multiple unsuccessful suppliers that have not been admitted to the warehouse and that correspond to the supplier's admission application; Determine the reliability parameters and suitability parameters of each unsuccessful supplier; Calculate the average value of the parameter distance between the reliability parameter corresponding to the supplier object and the reliability parameters of all unsuccessful suppliers to obtain the reliability gap parameter; Calculate the average of the parameter distances between the fitness parameter corresponding to the supplier object and the fitness parameters of all unsuccessful suppliers to obtain the fitness gap parameter; The weighted average of the reliability gap parameter and the adaptation gap parameter is calculated to obtain the fraud degree parameter corresponding to the supplier object.

[0046] It can be seen that through the above optional embodiments, the reliability and adaptability of the remaining unsuccessful suppliers in the inventory are used to determine the cost level of the supplier object based on distance calculation, which is used for subsequent screening of suppliers into the inventory, thereby helping to improve the efficiency and effectiveness of project bidding management and provide assistance for the smooth progress of the project.

[0047] As an optional embodiment, in the above steps, determining the warehousing result corresponding to the supplier object according to the reliability parameter, the adaptation parameter and the fraud parameter includes: When the reliability parameter is greater than the reliability parameter threshold, the adaptation parameter is greater than the adaptation parameter threshold, and the fraud parameter is less than the first fraud parameter threshold, the warehousing result of the supplier object is determined to be approved for warehousing; otherwise, the warehousing result of the supplier object is determined to be approved for warehousing; When the fraud degree parameter is greater than the second fraud parameter threshold, a warning message corresponding to the supplier object is sent to a designated approval user terminal device.

[0048] It can be seen that through the above optional embodiments, it is possible to determine whether to add a supplier to the warehouse or issue a warning based on the threshold judgment rules, thereby improving the screening accuracy and early warning effect of suppliers, thereby assisting in improving the efficiency and effectiveness of project bidding management and providing assistance for the smooth progress of the project.

[0049] Embodiment 2 See also Figure 2 , Figure 2 This is a schematic diagram of the structure of a data processing system for project bidding management disclosed in an embodiment of the present invention. Figure 2The data processing system for project bidding management described above can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the data processing system for engineering bidding management may include: The acquisition module 201 is used to acquire the tender planning request for the target engineering project sent by the tender planning device.

[0050] Optionally, the bidding planning request is used to be sent to at least one approval end device according to preset approval rules.

[0051] The approval module 202 is used to generate a bidding plan corresponding to the target engineering project according to the bidding planning request and the approval result of the approval end device. The determination module 203 is used to obtain the bidding management application sent by the management device, and determine the bidding execution plan of the bidding management application according to the bidding management application and the corresponding approval rules. The execution module 204 is used to obtain the supplier management application sent by the management device, and determine a management execution plan for the supplier management application according to the supplier management application and the corresponding supplier historical data.

[0052] It can be seen that the above-mentioned embodiments of the invention can approve bidding planning requests or management requests through preset approval rules and approval results, and perform supplier management based on the supplier's historical data, thereby being able to combine data processing technology to achieve comprehensive engineering bidding management, improve the efficiency and effectiveness of engineering bidding management, and provide assistance for the smooth development of the project.

[0053] As an optional embodiment, the bidding planning request includes the project name, planning person in charge, planning compiler, planning date requirement, planning approval requirement, bidding requirements and bidding plan; the bidding requirements include the bidding project, bidding subject, section name, section scope, bidding classification and bidding responsible person.

[0054] It can be seen that through the above optional embodiments, the content of the bidding planning request is clarified, which can make the bidding planning more comprehensive and accurate, so as to improve the information sufficiency of the final bidding plan, thereby helping to improve the efficiency and effectiveness of project bidding management and provide assistance for the smooth progress of the project.

[0055] As an optional embodiment, the bidding management application includes at least one of a new bidding application, a bidding document preparation application, a qualification review application, a bidding document processing application, a bid opening and evaluation application, a winning bid notification application, and a document archiving application.

[0056] It can be seen that through the above optional embodiments, the types of bidding management applications are clarified, and more comprehensive bidding management functions can be implemented, thereby assisting in improving the efficiency and effectiveness of project bidding management and providing assistance for the smooth progress of the project.

[0057] As an optional embodiment, the supplier management application includes at least one of an application for adding a new supplier, an application for inspecting a supplier, an application for approving a supplier, an application for admitting a supplier to a warehouse, an application for viewing supplier information, an application for modifying supplier information, and an application for blacklisting a supplier.

[0058] It can be seen that through the above optional embodiments, the types of supplier management applications are clarified, and a more comprehensive supplier management function can be implemented to effectively manage various supplier-related needs, thereby assisting in improving the efficiency and effectiveness of project bidding management and providing assistance for the smooth progress of the project.

[0059] As an optional embodiment, the execution module determines the specific manner of the management execution plan of the supplier management application according to the supplier management application and the corresponding supplier historical data, including: When the supplier management application is a supplier entry application, obtain the historical approval data, historical submission documents, historical credit data and historical service object data of the supplier object corresponding to the supplier entry application; According to the trained neural network model, based on historical approval data, historical submission documents, historical credit data and historical service object data, the reliability parameters and adaptability parameters of the supplier object are determined; Determine the fraud degree parameter corresponding to the supplier object based on the reliability parameter and the adaptability parameter, as well as the information of the remaining unselected suppliers corresponding to the supplier entry application; According to the reliability parameter, adaptation parameter and fraud degree parameter, the warehousing result corresponding to the supplier object is determined.

[0060] It can be seen that through the above-mentioned optional embodiments, the reliability, adaptability and cost can be determined based on the supplier's historical approval data, submitted documents, credit data and service objects, so as to finally determine the supplier's warehousing results and improve the accuracy of supplier screening, thereby assisting in improving the efficiency and effectiveness of project bidding management and providing assistance for the smooth progress of the project.

[0061] As an optional embodiment, the execution module determines the reliability parameter and the adaptability parameter of the supplier object according to the trained neural network model based on the historical approval data, the historical submission documents, the historical credit data and the historical service object data, including: Inputting historical submission documents and historical credit data into a trained reliable prediction neural network to obtain a reliability parameter corresponding to the supplier object; optionally, the reliable prediction neural network is trained using a training data set including submission document data and credit data of multiple training suppliers and corresponding reliability annotations; The historical approval data and historical service object data are input into the trained adaptation prediction neural network to obtain the adaptation degree parameters corresponding to the supplier object; the adaptation prediction neural network is trained by a training data set including approval data and service object data of multiple training suppliers and corresponding adaptability annotations.

[0062] It can be seen that through the above-mentioned optional embodiments, the reliability and adaptability of the supplier can be determined respectively based on the reliable prediction neural network and the adaptive prediction neural network, so as to finally determine the supplier's warehousing results and improve the accuracy of supplier screening, thereby assisting in improving the efficiency and effectiveness of project bidding management and providing assistance for the smooth progress of the project.

[0063] As an optional embodiment, the execution module determines the specific method of the fraud degree parameter corresponding to the supplier object according to the reliability parameter and the adaptability parameter, and the information of the remaining unselected suppliers corresponding to the supplier entry application, including: Obtain multiple unsuccessful suppliers that have not been admitted to the warehouse and that correspond to the supplier's admission application; Determine the reliability parameters and suitability parameters of each unsuccessful supplier; Calculate the average value of the parameter distance between the reliability parameter corresponding to the supplier object and the reliability parameters of all unsuccessful suppliers to obtain the reliability gap parameter; Calculate the average of the parameter distances between the fitness parameter corresponding to the supplier object and the fitness parameters of all unsuccessful suppliers to obtain the fitness gap parameter; The weighted average of the reliability gap parameter and the adaptation gap parameter is calculated to obtain the fraud degree parameter corresponding to the supplier object.

[0064] It can be seen that through the above optional embodiments, the reliability and adaptability of the remaining unsuccessful suppliers in the inventory are used to determine the cost level of the supplier object based on distance calculation, which is used for subsequent screening of suppliers into the inventory, thereby helping to improve the efficiency and effectiveness of project bidding management and provide assistance for the smooth progress of the project.

[0065] As an optional embodiment, the execution module determines the specific manner of the warehousing result corresponding to the supplier object according to the reliability parameter, the adaptation parameter and the fraud parameter, including: When the reliability parameter is greater than the reliability parameter threshold, the adaptation parameter is greater than the adaptation parameter threshold, and the fraud parameter is less than the first fraud parameter threshold, the warehousing result of the supplier object is determined to be approved for warehousing; otherwise, the warehousing result of the supplier object is determined to be approved for warehousing; When the fraud degree parameter is greater than the second fraud parameter threshold, a warning message corresponding to the supplier object is sent to a designated approval user terminal device.

[0066] It can be seen that through the above optional embodiments, it is possible to determine whether to add a supplier to the warehouse or issue a warning based on the threshold judgment rules, thereby improving the screening accuracy and early warning effect of suppliers, thereby assisting in improving the efficiency and effectiveness of project bidding management and providing assistance for the smooth progress of the project.

[0067] Embodiment 3 See also Figure 3 , Figure 3 This is another data processing system for engineering bidding management disclosed in an embodiment of the present invention. Figure 3 The data processing system for project bidding management described is applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the data processing system for engineering bidding management may include: A memory 301 storing executable program codes; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the data processing method for engineering bidding management described in the first embodiment.

[0068] Embodiment 4 An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the data processing method for engineering bidding management described in the first embodiment.

[0069] Embodiment 5 An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the data processing method for engineering bidding management described in the first embodiment.

[0070] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0072] For the convenience of description, the above device is described by dividing it into various units according to its functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0073] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0074] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0075] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0077] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0078] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0079] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer 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 technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0080] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0081] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0082] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0083] Finally, it should be noted that the data processing method and system for engineering bidding management disclosed in the embodiment of the present invention only disclose the preferred embodiments of the present invention, which are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A data processing method for engineering bidding management, characterized in that: The method comprises: Obtaining a bidding planning request for a target engineering project sent by a bidding planning device; the bidding planning request is used to be sent to at least one approval end device according to a preset approval rule; Generate a bidding plan corresponding to the target engineering project according to the bidding planning request and the approval result of the approval terminal device; Obtaining a tender management application sent by a management device, and determining a tender execution plan for the tender management application according to the tender management application and corresponding approval rules; A supplier management application sent by a management device is obtained, and a management execution plan for the supplier management application is determined according to the supplier management application and corresponding supplier historical data.

2. The data processing method for engineering bidding management according to claim 1 is characterized in that: The bidding planning request includes the project name, planning person in charge, planning preparer, planning date requirements, planning approval requirements, bidding needs and bidding plan; the bidding needs include the bidding project, bidding subject, section name, section scope, bidding classification and bidding responsible person.

3. The data processing method for engineering bidding management according to claim 1 is characterized in that: The bidding management application includes at least one of a new bidding application, a bidding document preparation application, a qualification review application, a bidding document processing application, a bid opening and evaluation application, a winning bid notification application and a document archiving application.

4. The data processing method for engineering bidding management according to claim 1 is characterized in that: The supplier management application includes at least one of an application for adding a new supplier, an application for inspecting a supplier, an application for approving a supplier, an application for admitting a supplier into the warehouse, an application for viewing supplier information, an application for modifying supplier information, and an application for blacklisting a supplier.

5. The data processing method for engineering bidding management according to claim 4 is characterized in that: Determining a management execution plan for the supplier management application based on the supplier management application and corresponding supplier historical data includes: When the supplier management application is a supplier entry application, historical approval data, historical submission documents, historical credit data and historical service object data of the supplier object corresponding to the supplier entry application are obtained; Determine the reliability parameter and the adaptability parameter of the supplier object based on the trained neural network model and the historical approval data, the historical submission documents, the historical credit data and the historical service object data; Determine the fraud degree parameter corresponding to the supplier object according to the reliability parameter and the adaptation degree parameter, as well as the information of the remaining unselected suppliers corresponding to the supplier entry application; The warehousing result corresponding to the supplier object is determined according to the reliability parameter, the adaptation parameter and the fraud parameter.

6. The data processing method for engineering bidding management according to claim 5 is characterized in that: The step of determining the reliability parameter and the adaptability parameter of the supplier object based on the trained neural network model and the historical approval data, historical submission documents, historical credit data and historical service object data includes: Inputting the historical submission documents and the historical credit data into a trained reliable prediction neural network to obtain a reliability parameter corresponding to the supplier object; the reliable prediction neural network is trained by a training data set including submission document data and credit data of multiple training suppliers and corresponding reliability annotations; The historical approval data and the historical service object data are input into a trained adaptation prediction neural network to obtain the adaptation degree parameter corresponding to the supplier object; the adaptation prediction neural network is trained by a training data set including approval data and service object data of multiple training suppliers and corresponding adaptability annotations.

7. The data processing method for engineering bidding management according to claim 5 is characterized in that: The step of determining the counterfeiting degree parameter corresponding to the supplier object according to the reliability parameter and the adaptation degree parameter, as well as the information of the remaining unselected suppliers corresponding to the supplier entry application, includes: Obtaining the remaining multiple unsuccessful suppliers corresponding to the supplier's entry application; Determining the reliability parameter and the suitability parameter of each of the unsuccessful suppliers; Calculate the average value of the parameter distance between the reliability parameter corresponding to the supplier object and the reliability parameters of all the unsuccessful suppliers to obtain a reliability gap parameter; Calculate the average value of the parameter distance between the fitness parameter corresponding to the supplier object and the fitness parameters of all the unsuccessful suppliers to obtain a fitness gap parameter; The weighted average value of the reliability gap parameter and the adaptation gap parameter is calculated to obtain a counterfeiting degree parameter corresponding to the supplier object.

8. The data processing method for engineering bidding management according to claim 5 is characterized in that: The determining, according to the reliability parameter, the adaptation parameter and the fraud parameter, a warehousing result corresponding to the supplier object includes: When the reliability parameter is greater than the reliability parameter threshold, the adaptation parameter is greater than the adaptation parameter threshold, and the fraud parameter is less than the first fraud parameter threshold, determining that the warehousing result of the supplier object is approved for warehousing; otherwise, determining that the warehousing result of the supplier object is approved for warehousing; When the fraud degree parameter is greater than a second fraud parameter threshold, the warning information corresponding to the supplier object is sent to a designated approval user terminal device.

9. A data processing system for project bidding management, characterized in that: The system comprises: An acquisition module, used to acquire a tender planning request for a target engineering project sent by a tender planning device; the tender planning request is used to be sent to at least one approval end device according to a preset approval rule; An approval module, used to generate a bidding plan corresponding to the target engineering project according to the bidding planning request and the approval result of the approval terminal device; A determination module, used to obtain the tender management application sent by the management device, and determine the tender execution plan of the tender management application according to the tender management application and the corresponding approval rules; The execution module is used to obtain the supplier management application sent by the management device, and determine the management execution plan of the supplier management application according to the supplier management application and the corresponding supplier historical data.

10. A data processing system for engineering bidding management, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the data processing method for engineering bidding management as described in any one of claims 1-8.

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