Data Processing Method and System for Engineering Bidding Management

Abstract

The present invention discloses a data processing method and system for engineering tender management. The method includes: obtaining 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 terminal device according to a preset approval rule; generating a tender plan corresponding to the target engineering project according to the tender 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 the corresponding approval rule; obtaining a supplier management application sent by a management device, and determining a management execution plan for the supplier management application according to the supplier management application and the corresponding supplier historical data. It can be seen that the present invention can realize comprehensive engineering tender management by combining data processing technology, improve the efficiency and effect of engineering tender management, and provide help for the smooth progress 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 tender management. Background Art

[0002] With the increasing scale of construction engineering projects, the tender requirements for engineering projects are also getting higher and higher. How to ensure the smoothness and accuracy of the tender process while implementing a large number of engineering tenders is an important issue. Although the existing technologies partially combine data processing technologies to achieve some tender management, they do not make full use of approval rules and supplier historical data to improve the efficiency of tender management. It can be seen that the existing technologies have defects and need 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 tender management, which can combine data processing technologies to achieve comprehensive engineering tender management, improve the efficiency and effect of engineering tender management, and provide help for the smooth progress of projects.

[0004] To solve the above technical problem, in the first aspect of the present invention, a data processing method for engineering tender management is disclosed, and the method includes:

[0005] Obtain 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 device according to preset approval rules;

[0006] Generate a tender plan corresponding to the target engineering project according to the tender planning request and the approval result of the approval device;

[0007] Obtain a tender management application sent by a management device, and determine a tender execution plan for the tender management application according to the tender management application and the corresponding approval rules;

[0008] Obtain a supplier management application sent by a 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.

[0009] As an optional implementation manner, in the first aspect of the present invention, the tender planning request includes a project name, a planning person in charge, a planning compiler, a planning date requirement, a planning approval requirement, a tender requirement, and a tender plan; the tender requirement includes a tender project, a tender entity, a section name, a section scope, a tender classification, and a tender responsible person.

[0010] 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.

[0011] 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.

[0012] 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:

[0013] 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;

[0014] 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;

[0015] 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;

[0016] The warehousing result corresponding to the supplier object is determined according to the reliability parameter, the adaptation parameter and the fraud parameter.

[0017] 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:

[0018] 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;

[0019] Input the historical approval data and the historical service object data into the 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 the approval data, service object data, and corresponding adaptability annotations of multiple training suppliers.

[0020] As an optional implementation manner, in the first aspect of the present invention, the determining the fraud degree parameter corresponding to the supplier object according to the reliability degree parameter, the adaptation degree parameter, and the information of the remaining un-included rejected suppliers corresponding to the supplier warehousing application includes:

[0021] Obtain the remaining multiple un-included rejected suppliers corresponding to the supplier warehousing application;

[0022] Determine the reliability degree parameter and the adaptation degree parameter of each of the rejected suppliers;

[0023] Calculate the average value of the parameter distances between the reliability degree parameter corresponding to the supplier object and the reliability degree parameters of all the rejected suppliers to obtain a reliability gap parameter;

[0024] Calculate the average value of the parameter distances between the adaptation degree parameter corresponding to the supplier object and the adaptation degree parameters of all the rejected suppliers to obtain an adaptation gap parameter;

[0025] Calculate the weighted sum average value of the reliability gap parameter and the adaptation gap parameter to obtain the fraud degree parameter corresponding to the supplier object.

[0026] As an optional implementation manner, in the first aspect of the present invention, the determining the warehousing result corresponding to the supplier object according to the reliability degree parameter, the adaptation degree parameter, and the fraud degree parameter includes:

[0027] When the reliability degree parameter is greater than the reliability parameter threshold, the adaptation degree parameter is greater than the adaptation parameter threshold, and the fraud degree parameter is less than the first fraud parameter threshold, determine that the warehousing result of the supplier object is approved for warehousing, otherwise determine that the warehousing result of the supplier object is approved for warehousing;

[0028] When the fraud degree parameter is greater than the second fraud parameter threshold, send the warning information corresponding to the supplier object to the specified approval user terminal device.

[0029] A second aspect of the embodiments of the present invention discloses a data processing system for engineering bidding management, and the system includes:

[0030] An acquisition module, configured to acquire 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 terminal device according to a preset approval rule;

[0031] An approval module, configured 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;

[0032] A determination module, configured to acquire a bidding management application sent by a management device, and determine a bidding execution plan for the bidding management application according to the bidding management application and the corresponding approval rule;

[0033] An execution module, configured to acquire a supplier management application sent by a management device, and determine a management execution plan for the supplier management application according to the supplier management application and the corresponding historical supplier data.

[0034] As an optional implementation manner, in the second aspect of the present invention, the bidding planning request includes a project name, a planning person in charge, a planning compiler, a planning date requirement, a planning approval requirement, a bidding requirement, and a bidding plan; the bidding requirement includes a bidding project, a bidding entity, a section name, a section scope, a bidding classification, and a bidding responsible person.

[0035] As an optional implementation manner, in the second aspect of the present invention, the bidding management application includes at least one of a new bidding application, a bidding document compilation application, a qualification review application, a bidding document processing application, an opening and bid evaluation application, a winning bid notice application, and a data archiving application.

[0036] As an optional implementation manner, in the second aspect of the present invention, the supplier management application includes at least one of a new supplier application, a supplier inspection application, a supplier approval application, a supplier warehousing application, a supplier information viewing application, a supplier information modification application, and a supplier blacklisting application.

[0037] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the execution module determines the management execution plan for the supplier management application according to the supplier management application and the corresponding historical supplier data includes:

[0038] When the supplier management application is a supplier warehousing application, acquire the historical approval data, historical submitted documents, historical credit data, and historical service object data of the supplier object corresponding to the supplier warehousing application;

[0039] According to the trained neural network model, based on the historical approval data, historical submitted documents, historical credit data, and historical service object data, determine the reliability parameter and adaptability parameter of the supplier object;

[0040] Determine the fraud degree parameter corresponding to the supplier object according to the reliability degree parameter, the adaptation degree parameter, and the information of the remaining un-included rejected suppliers corresponding to the supplier warehousing application;

[0041] Determine the warehousing result corresponding to the supplier object according to the reliability degree parameter, the adaptation degree parameter, and the fraud degree parameter.

[0042] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the execution module determines the reliability degree parameter and the adaptation degree parameter of the supplier object based on the historical approval data, historical submission documents, historical credit data, and historical service object data according to the trained neural network model includes:

[0043] Input the historical submission documents and the historical credit data into the trained reliable prediction neural network to obtain the reliability degree parameter corresponding to the supplier object; the reliable prediction neural network is trained through a training data set including the submission document data and credit data of multiple training suppliers and the corresponding reliability annotations;

[0044] Input the historical approval data and the historical service object data into the trained adaptation prediction neural network to obtain the adaptation degree parameter corresponding to the supplier object; the adaptation prediction neural network is trained through a training data set including the approval data and service object data of multiple training suppliers and the corresponding adaptability annotations.

[0045] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the execution module determines the fraud degree parameter corresponding to the supplier object according to the reliability degree parameter, the adaptation degree parameter, and the information of the remaining un-included rejected suppliers corresponding to the supplier warehousing application includes:

[0046] Obtain the remaining un-included multiple rejected suppliers corresponding to the supplier warehousing application;

[0047] Determine the reliability degree parameter and the adaptation degree parameter of each of the rejected suppliers;

[0048] Calculate the average value of the parameter distances between the reliability degree parameter corresponding to the supplier object and the reliability degree parameters of all the rejected suppliers to obtain a reliability gap parameter;

[0049] Calculate the average value of the parameter distances between the adaptation degree parameter corresponding to the supplier object and the adaptation degree parameters of all the rejected suppliers to obtain an adaptation gap parameter;

[0050] Calculate the weighted sum average of the reliability gap parameter and the adaptation gap parameter to obtain the fraud degree parameter corresponding to the supplier object.

[0051] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the execution module determines the warehousing result corresponding to the supplier object according to the reliability parameter, the adaptation parameter, and the fraud degree parameter includes:

[0052] When the reliability parameter is greater than the reliability parameter threshold, the adaptation parameter is greater than the adaptation parameter threshold, and the fraud degree parameter is less than the first fraud parameter threshold, determine that the warehousing result of the supplier object is approved for warehousing; otherwise, determine that the warehousing result of the supplier object is approved for warehousing;

[0053] When the fraud degree parameter is greater than the second fraud parameter threshold, send the warning information corresponding to the supplier object to the specified approval client device.

[0054] The third aspect of the present invention discloses another data processing system for engineering tender management, and the system includes:

[0055] A memory storing executable program code;

[0056] A processor coupled to the memory;

[0057] The processor calls the executable program code stored in the memory and executes some or all of the steps in the data processing method for engineering tender management disclosed in the first aspect of the present invention.

[0058] The fourth aspect of the present invention discloses a computer storage medium, and the computer storage medium stores computer instructions, which are used to execute some or all of the steps in the data processing method for engineering tender management disclosed in the first aspect of the present invention when called.

[0059] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0060] The present invention can approve the tender planning request or management request through preset approval rules and approval results, and perform the management of suppliers based on the historical data of the suppliers, so as to realize comprehensive engineering tender management in combination with data processing technology, improve the efficiency and effect of engineering tender management, and provide help for the smooth progress of the project. Description of the Drawings

[0061] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0062] Figure 1 It is a schematic flowchart of a data processing method for engineering tender management disclosed in an embodiment of the present invention.

[0063] Figure 2 It is a schematic structural diagram of a data processing system for engineering tender management disclosed in an embodiment of the present invention.

[0064] Figure 3 It is a schematic structural diagram of another data processing system for engineering tender management disclosed in an embodiment of the present invention. Detailed implementation manners

[0065] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0066] The terms "first", "second", etc. in the specification and claims of the present invention and the above drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. 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 optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or equipment.

[0067] Referring to "embodiments" herein means that the specific features, structures or characteristics described in connection with the embodiments can be included in at least one embodiment of the present invention. The phrase appears in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0068] The present invention discloses a data processing method and system for engineering tender management, which can approve tender planning requests or management requests through preset approval rules and approval results, and perform supplier management based on the historical data of suppliers, so as to realize comprehensive engineering tender management by combining data processing technologies, improve the efficiency and effect of engineering tender management, and provide assistance for the smooth progress of projects. The following will be described in detail respectively.

[0069] Embodiment 1

[0070] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a data processing method for engineering tender management disclosed in an embodiment of the present invention. Among them, Figure 1 the described data processing method for engineering tender management 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). As Figure 1 shown, the data processing method for engineering tender management may include the following operations:

[0071] 101. Obtain a tender planning request for a target engineering project sent by a tender planning device.

[0072] Optionally, the tender planning request is used to be sent to at least one approval terminal device according to preset approval rules.

[0073] 102. Generate a tender plan corresponding to the target engineering project according to the tender planning request and the approval result of the approval terminal device.

[0074] 103. Obtain a tender management application sent by a management device, and determine a tender execution plan for the tender management application according to the tender management application and the corresponding approval rules.

[0075] 104. Obtain a supplier management application sent by a 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.

[0076] It can be seen that the above-mentioned invention embodiments can approve tender planning requests or management requests through preset approval rules and approval results, and perform supplier management based on the historical data of suppliers, so as to realize comprehensive engineering tender management by combining data processing technologies, improve the efficiency and effect of engineering tender management, and provide assistance for the smooth progress of projects.

[0077] As an optional embodiment, in the above steps, the bidding planning request includes the project name, the person in charge of planning, the person preparing the plan, the requirements for the planning date, the requirements for planning approval, the bidding requirements, and the bidding plan; the bidding requirements include the bidding project, the bidding entity, the section name, the section scope, the bidding classification, and the person responsible for bidding.

[0078] It can be seen that through the above optional embodiment, 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, and thus can assist in improving the efficiency and effect of project bidding management and provide help for the smooth progress of the project.

[0079] 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, an opening and evaluation application, a winning bid notice application, and a data archiving application.

[0080] It can be seen that through the above optional embodiment, the types of bidding management applications are clarified, which can realize more comprehensive bidding management functions, and thus can assist in improving the efficiency and effect of project bidding management and provide help for the smooth progress of the project.

[0081] As an optional embodiment, in the above steps, the supplier management application includes at least one of a new supplier application, a supplier inspection application, a supplier approval application, a supplier warehousing application, a supplier information viewing application, a supplier information modification application, and a supplier blacklisting application.

[0082] It can be seen that through the above optional embodiment, the types of supplier management applications are clarified, which can realize more comprehensive supplier management functions to effectively manage various requirements related to suppliers, and thus can assist in improving the efficiency and effect of project bidding management and provide help for the smooth progress of the project.

[0083] As an optional embodiment, in the above steps, according to the supplier management application and the corresponding historical supplier data, determine the management execution plan for the supplier management application, including:

[0084] When the supplier management application is a supplier warehousing application, obtain the historical approval data, historical submitted documents, historical credit data, and historical service object data of the supplier object corresponding to the supplier warehousing application;

[0085] According to the trained neural network model, based on the historical approval data, historical submitted documents, historical credit data, and historical service object data, determine the reliability parameter and adaptability parameter of the supplier object;

[0086] Determine the fraud degree parameter corresponding to the supplier object according to the reliability degree parameter, the adaptation degree parameter, and the information of the remaining un-included rejected suppliers corresponding to the supplier's warehousing application;

[0087] Determine the warehousing result corresponding to the supplier object according to the reliability degree parameter, the adaptation degree parameter, and the fraud degree parameter.

[0088] It can be seen that through the above optional embodiments, it is possible to determine the reliability degree, the adaptation degree, and the fraud degree based on the historical approval data, the submitted documents, the credit data, and the service objects of the supplier, so as to finally determine the warehousing result of the supplier, improve the accuracy of supplier screening, and thus be able to assist in improving the efficiency and effect of project bidding management and provide help for the smooth progress of the project.

[0089] As an optional embodiment, in the above steps, according to the trained neural network model, based on the historical approval data, the historical submitted documents, the historical credit data, and the historical service object data, to determine the reliability degree parameter and the adaptation degree parameter of the supplier object, including:

[0090] Input the historical submitted documents and the historical credit data into the trained reliability prediction neural network to obtain the reliability degree parameter corresponding to the supplier object; optionally, the reliability prediction neural network is trained through a training data set including the submitted document data and credit data of multiple training suppliers and the corresponding reliability annotations;

[0091] Input the historical approval data and the historical service object data into the trained adaptation prediction neural network to obtain the adaptation degree parameter corresponding to the supplier object; the adaptation prediction neural network is trained through a training data set including the approval data and service object data of multiple training suppliers and the corresponding adaptability annotations.

[0092] It can be seen that through the above optional embodiments, it is possible to respectively determine the reliability degree and the adaptation degree of the supplier based on the reliability prediction neural network and the adaptation prediction neural network, so as to be used to finally determine the warehousing result of the supplier, improve the accuracy of supplier screening, and thus be able to assist in improving the efficiency and effect of project bidding management and provide help for the smooth progress of the project.

[0093] As an optional embodiment, in the above steps, according to the reliability degree parameter, the adaptation degree parameter, and the information of the remaining un-included rejected suppliers corresponding to the supplier's warehousing application, to determine the fraud degree parameter corresponding to the supplier object, including:

[0094] Obtain multiple remaining un-included rejected suppliers corresponding to the supplier's warehousing application;

[0095] Determine the reliability degree parameter and the adaptation degree parameter of each rejected supplier;

[0096] Calculate the average value of the parameter distance between the reliability parameter corresponding to the supplier object and the reliability parameters of all rejected suppliers to obtain the reliability gap parameter;

[0097] Calculate the average value of the parameter distance between the adaptability parameter corresponding to the supplier object and the adaptability parameters of all rejected suppliers to obtain the adaptability gap parameter;

[0098] Calculate the weighted sum average of the reliability gap parameter and the adaptability gap parameter to obtain the fraud degree parameter corresponding to the supplier object.

[0099] It can be seen that through the above optional embodiments, the reliability and adaptability of the remaining rejected suppliers for warehousing are based on distance calculation to determine the cost of the supplier object for subsequent warehousing screening of suppliers, thereby being able to assist in improving the efficiency and effect of engineering tender management and providing help for the smooth progress of the project.

[0100] As an optional embodiment, in the above steps, according to the reliability parameter, adaptability parameter, and fraud degree parameter, determine the warehousing result corresponding to the supplier object, including:

[0101] When the reliability parameter is greater than the reliability parameter threshold, the adaptability parameter is greater than the adaptability parameter threshold, and the fraud degree parameter is less than the first fraud parameter threshold, determine that the warehousing result of the supplier object is approved for warehousing, otherwise determine that the warehousing result of the supplier object is approved for warehousing;

[0102] When the fraud degree parameter is greater than the second fraud parameter threshold, send the warning information corresponding to the supplier object to the designated approval user terminal device.

[0103] It can be seen that through the above optional embodiments, it is possible to determine whether to warehouse or warn the supplier based on the threshold judgment rule, improve the screening accuracy and early warning effect of the supplier, thereby being able to assist in improving the efficiency and effect of engineering tender management and providing help for the smooth progress of the project.

[0104] Embodiment Two

[0105] Please refer to Figure 2 , Figure 2 is a schematic structural diagram of a data processing system for engineering tender management disclosed in an embodiment of the present invention. Among them, Figure 2 The described data processing system for engineering tender management 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). As Figure 2 shown, the data processing system for engineering tender management may include:

[0106] An acquisition module 201, configured to acquire a bidding planning request for a target engineering project sent by a bidding planning device.

[0107] Optionally, the bidding planning request is sent to at least one approval terminal device according to a preset approval rule.

[0108] An approval module 202, configured 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.

[0109] A determination module 203, configured to acquire a bidding management application sent by a management device, and determine a bidding execution plan for the bidding management application according to the bidding management application and the corresponding approval rule.

[0110] An execution module 204, configured to acquire a supplier management application sent by a management device, and determine a management execution plan for the supplier management application according to the supplier management application and the corresponding historical supplier data.

[0111] It can be seen that the above-described invention embodiments can approve a bidding planning request or a management request through a preset approval rule and an approval result, and perform management of a supplier based on the historical data of the supplier, so as to realize comprehensive engineering bidding management in combination with data processing technology, improve the efficiency and effect of engineering bidding management, and provide assistance for the smooth progress of the project.

[0112] As an optional embodiment, the bidding planning request includes a project name, a planning person in charge, a planning compiler, a planning date requirement, a planning approval requirement, a bidding requirement, and a bidding plan; the bidding requirement includes a bidding project, a bidding entity, a section name, a section scope, a bidding classification, and a bidding responsible person.

[0113] It can be seen that through the above optional embodiment, 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 assisting in improving the efficiency and effect of engineering bidding management and providing assistance for the smooth progress of the project.

[0114] As an optional embodiment, the bidding management application includes at least one of a new bidding application, a bidding document compilation application, a qualification review application, a bidding document processing application, an opening and bid evaluation application, a winning bid notice application, and a data filing application.

[0115] It can be seen that through the above optional embodiment, the type of the bidding management application is clarified, which can realize a more comprehensive bidding management function, thereby assisting in improving the efficiency and effect of engineering bidding management and providing assistance for the smooth progress of the project.

[0116] 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.

[0117] 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.

[0118] 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:

[0119] 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;

[0120] 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;

[0121] 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;

[0122] According to the reliability parameters, adaptation parameters and fraudulent degree parameters, the warehousing result corresponding to the supplier object is determined.

[0123] 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.

[0124] 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:

[0125] Input historical submission files and historical credit data into the trained reliable prediction neural network to obtain the reliability parameter corresponding to the supplier object; optionally, the reliable prediction neural network is trained through a training data set including submission file data, credit data, and corresponding reliability annotations of multiple training suppliers;

[0126] Input historical approval data and historical service object data into the trained adaptation prediction neural network to obtain the adaptation parameter corresponding to the supplier object; the adaptation prediction neural network is trained through a training data set including approval data, service object data, and corresponding adaptability annotations of multiple training suppliers.

[0127] It can be seen that through the above optional embodiments, the reliability and adaptability of the supplier can be determined based on the reliable prediction neural network and the adaptation prediction neural network respectively, so as to finally determine the warehousing result of the supplier, improve the accuracy of supplier screening, and thus assist in improving the efficiency and effect of project bidding management and provide help for the smooth progress of the project.

[0128] As an optional embodiment, the specific method for the execution module to determine the fraud parameter corresponding to the supplier object according to the reliability parameter, the adaptation parameter, and the information of the remaining unwarehoused rejected suppliers corresponding to the supplier warehousing application includes:

[0129] Obtain the remaining unwarehoused multiple rejected suppliers corresponding to the supplier warehousing application;

[0130] Determine the reliability parameter and the adaptation parameter of each rejected supplier;

[0131] Calculate the average value of the parameter distances between the reliability parameter corresponding to the supplier object and the reliability parameters of all rejected suppliers to obtain the reliability gap parameter;

[0132] Calculate the average value of the parameter distances between the adaptation parameter corresponding to the supplier object and the adaptation parameters of all rejected suppliers to obtain the adaptation gap parameter;

[0133] Calculate the weighted sum average of the reliability gap parameter and the adaptation gap parameter to obtain the fraud parameter corresponding to the supplier object.

[0134] It can be seen that through the above optional embodiments, the reliability and adaptability of the remaining unwarehoused rejected suppliers are used to determine the fraud degree of the supplier object based on distance calculation for subsequent warehousing screening of the supplier, so as to assist in improving the efficiency and effect of project bidding management and provide help for the smooth progress of the project.

[0135] As an optional embodiment, the specific manner in which the execution module determines the warehousing result corresponding to the supplier object according to the reliability parameter, the adaptation degree parameter, and the forgery degree parameter includes:

[0136] When the reliability parameter is greater than the reliability parameter threshold, the adaptation degree parameter is greater than the adaptation parameter threshold, and the forgery degree parameter is less than the first forgery parameter threshold, it is determined that the warehousing result of the supplier object is approved for warehousing; otherwise, it is determined that the warehousing result of the supplier object is approved for warehousing;

[0137] When the forgery degree parameter is greater than the second forgery parameter threshold, the warning information corresponding to the supplier object is sent to the designated approval user terminal device.

[0138] It can be seen that through the above optional embodiments, it is possible to determine whether to warehouse a supplier or give a warning based on the threshold judgment rule, improve the screening accuracy and early warning effect of the supplier, and thus be able to assist in improving the efficiency and effect of engineering bidding management and provide help for the smooth progress of the project.

[0139] Embodiment III

[0140] Please refer to Figure 3 , Figure 3 which is another data processing system for engineering bidding management disclosed in the embodiments of the present invention. Figure 3 The data processing system for engineering 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). As Figure 3 shown, the data processing system for engineering bidding management may include:

[0141] A memory 301 storing executable program code;

[0142] A processor 302 coupled to the memory 301;

[0143] Among them, 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 Embodiment I.

[0144] Embodiment IV

[0145] The embodiments of the present invention disclose a computer-readable storage medium, which stores 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 Embodiment I.

[0146] Embodiment V

[0147] 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 cause a computer to execute the steps of the data processing method for engineering tender management described in Embodiment 1.

[0148] The above describes specific embodiments of this specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily have to be performed in the specific order or continuous order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0149] The systems, devices, modules, or units illustrated in the above embodiments may be specifically 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 any combination of these devices.

[0150] For convenience of description, when describing the above devices, they are described separately as various units according to their functions. Of course, when implementing this specification, the functions of each unit may be implemented in the same or multiple software and / or hardware.

[0151] Those skilled in the art should understand that the embodiments of this specification may be provided as a method, a system, or a computer program product. Therefore, the embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0152] This specification is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of this specification. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementation in the processFigure 1 means for the functions specified in one process or multiple processes and / or boxes Figure 1 or multiple boxes.

[0153] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device that implements the functions specified in one process Figure 1 or multiple processes and / or boxes Figure 1 or multiple boxes.

[0154] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 or multiple processes and / or boxes Figure 1 or multiple boxes.

[0155] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0156] The memory may include non-permanent memory in the computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of, for example, read-only memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

[0157] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can store information by any method or technology. The 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 technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0158] It should also be noted that the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising said element.

[0159] This specification can 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 can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0160] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. 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 for the relevant parts, reference can be made to the description of the method embodiment.

[0161] Finally, it should be noted that: what is disclosed in a data processing method and system for engineering tender management disclosed in the embodiments of the present invention is only the preferred embodiments of the present invention, which are only used to illustrate the technical solutions of the present invention and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the 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; Acquire a supplier management application sent by a management device, and determine a management execution plan for the supplier management application according to the supplier management application and 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 parameter.

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 1 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.

6. The data processing method for engineering bidding management according to claim 1 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.

7. The data processing method for engineering bidding management according to claim 1 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.

8. 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, 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 parameter.

9. A data processing system for project 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 1-7.

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