An intelligent dispatching method based on AI engineering project big data

Abstract

The application discloses an intelligent order dispatching method based on AI engineering project big data and concretely relates to the technical field of engineering project management.The application quantitatively analyzes the relationship between the associated engineering quantity, the latest order processing duration and the engineering emergency degree, and proposes a dynamic priority adjustment mechanism which can optimize the engineering project order sorting according to the engineering project emergency degree factor, thereby solving the problem of weak concurrent processing capability of the existing intelligent order dispatching method.The application monitors the order load condition of the engineering service party in real time, avoids dispatching the engineering project order to the engineering service party with the order load, and improves the efficiency and accuracy of order dispatching.The application calculates the matching degree of each index through feature matching, lists the candidate engineering service party list according to the comprehensive matching degree calculated from the matching degree of each index, and adopts a multi-level screening rule to determine the final engineering service party to realize accurate order dispatching and ensure the execution quality of the engineering project.

Description

Technical Field

[0001] This invention relates to the field of AI engineering project big data technology, and more specifically, to an intelligent order dispatching method and system based on AI engineering project big data. Background Technology

[0002] Traditional engineering project dispatching methods mainly rely on manual scheduling. Manual scheduling is usually done by experienced project managers who make subjective judgments and allocate tasks based on the service provider's qualifications, geographical location, and historical cooperation records. This method can be flexibly adjusted according to the actual situation.

[0003] Existing intelligent project dispatching methods filter eligible service providers based on project type and geographical location. They then use manually set priority rules to filter service providers based on quality standards, selecting the service provider with the highest quality standard value as the target. If multiple quality standard values ​​exist, the method filters based on duration, selecting the service provider with the shortest required duration. If multiple duration values ​​also exist, the method filters based on distance, selecting the closest service provider. This method relies entirely on pre-set rules and can quickly process large numbers of orders, significantly improving efficiency compared to manual project dispatching.

[0004] However, existing project dispatching methods have significant limitations: while relying on pre-defined rules improves efficiency, they are still difficult to adapt to the dynamic changes and complex needs of projects, and have weak concurrent processing capabilities; the lack of effective monitoring of the real-time status of project service providers can easily lead to excessive order load on project service providers, potentially resulting in excessively long project execution delays; and the use of relatively simple filtering rules cannot adapt to the complex needs of some projects, resulting in inaccurate matching and the inability to fully guarantee the quality of project execution. Therefore, an optimized dispatching method is needed. Summary of the Invention

[0005] To overcome the problems of weak concurrent processing capabilities, excessively long project execution delays, and inability to fully guarantee the quality of project execution, this invention provides an intelligent dispatching method and system based on AI-powered big data for engineering projects.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent task dispatching method based on AI engineering project big data, comprising the following steps:

[0007] S1. Receive engineering project orders and identify key information about the engineering projects from various orders;

[0008] S2. Obtain basic information, capability information, service information, status information, and risk information of the engineering service provider;

[0009] S3. Sort the engineering project orders in descending order according to their urgency, and assign the engineering project orders in the order of order.

[0010] S4. Extract the engineering project order and engineering service provider's engineering feature data;

[0011] S5. Based on the extracted engineering project orders and engineering service providers' engineering feature data, calculate the matching degree of quality standards, technical requirements, schedule, material loss rate, and risk between each engineering service provider and the engineering project order.

[0012] S6. Calculate the overall matching degree between each engineering service provider and the engineering project order based on the matching degree of quality standards, technical requirements, schedule, material loss rate, and risk, and generate a list of candidate engineering service providers.

[0013] S7. Based on the list of candidate engineering service providers, generate target engineering service providers according to the preset screening rules and send order information. After the order is accepted, lock the order. If the order is not accepted, regenerate the target engineering service provider and send the order.

[0014] Preferably, the project order information in step S1 includes basic information, core requirements, and auxiliary information. The basic information includes the project name, number, client name, and contact information; the core requirements include the project type, number of associated tasks, specific project location, expected processing time, latest order processing time, technical requirements, quality standards, and material loss rate; and the auxiliary information includes special requirements and acceptance methods.

[0015] Preferably, in step S2, the basic information of the engineering service provider includes the location and contact information of the engineering service provider; the capability information includes the qualification certificates and skill certificates of the engineering service provider; the service information is the historical engineering project records of the engineering service provider; the status information is the real-time order load of the engineering service provider; and the risk information is the number of times the engineering service provider or its legal entity has been involved in legal disputes in the past three years.

[0016] Preferably, the specific formula for calculating the urgency level X of the project order in step S3 is as follows: In the formula, Ma represents the number of associated engineering projects, Ts represents the latest order processing time, and the engineering project orders are sorted in descending order according to the calculated urgency of the engineering project orders, and the engineering project orders are assigned in order of order.

[0017] Preferably, the engineering characteristic data of the engineering project order in step S4 includes quality standards, technical requirements, processing period and material loss rate; the engineering characteristic data of the engineering service provider includes qualification certificates, skill certificates, number of completed historical engineering projects, expected period of historical engineering projects, actual period of historical engineering projects, material loss rate of historical engineering projects and the number of legal disputes involving the individual or corporate entity of the engineering service provider in the past three years.

[0018] Preferably, the matching degree calculation steps for each indicator in step S5 are as follows:

[0019] S51. Calculate the quality standard matching degree Sqi of the i-th engineering service provider. The specific calculation formula is as follows: In the formula, Nai is the number of historical engineering projects completed by the i-th engineering service provider, and Saij is the quality coefficient of the j-th historical engineering project of the i-th engineering service provider. When the quality standard of the j-th historical engineering project of the i-th engineering service provider meets the standard, the quality coefficient Saij = 1; when the quality standard of the j-th historical engineering project of the i-th engineering service provider does not meet the standard, the quality coefficient Saij = 0.

[0020] S52. Calculate the technical requirement matching degree Sti of the i-th engineering service provider. The calculation formula is: In the formula, Nmi is the number of technical requirements of the engineering project order that the i-th engineering service provider satisfies, and Nt is the total number of technical requirements of the engineering project order;

[0021] S53. Calculate the schedule matching degree Swi of the i-th project service provider. The calculation formula is: In the formula, Emin is the minimum value of the average historical project processing efficiency of the i-th engineering service provider, Emax is the maximum value of the average historical project processing efficiency of the i-th engineering service provider, and Eai is the average historical project processing efficiency of the i-th engineering service provider. The specific calculation formula is as follows: Eij represents the processing efficiency of the j-th historical project by the i-th engineering service provider, and the specific calculation formula is as follows: In the formula, Teij is the expected construction period of the j-th historical project of the i-th engineering service provider, and Taij is the actual construction period of the j-th historical project of the i-th engineering service provider.

[0022] S54. Calculate the material loss rate matching degree Sai for the i-th engineering service provider. The calculation formula is: In the formula, Fmin is the minimum average material loss rate of the historical projects of the i-th engineering service provider, Fmax is the maximum average material loss rate of the historical projects of the i-th engineering service provider, and Fai is the average material loss rate of the i-th engineering service provider. The calculation formula is as follows: Fij represents the material loss rate of the j-th historical engineering project of the i-th engineering service provider;

[0023] S55. Calculate the risk matching degree Sri of the i-th engineering service provider. The calculation formula is as follows: In the formula, Hai represents the number of legal disputes involving the i-th individual or corporate entity in the past three years, Hmin represents the minimum number of legal disputes involving all individual or corporate entities, and Hmax represents the maximum number of legal disputes involving all individual or corporate entities.

[0024] Preferably, in step S6, the comprehensive matching degree is calculated based on the matching degree of quality standards, technical requirements, construction period, material loss rate, and risk. For companies with historical project records, the specific formula for calculating the comprehensive matching degree Sc1 is as follows: For companies with no historical engineering project records, the formula for calculating the overall matching degree Sc2 is as follows: Based on the calculated overall matching degree, a list of engineering service providers is generated in descending order.

[0025] Preferably, in step S7, the candidate engineering service providers are initially screened based on the project type of the engineering project orders, retaining only those with the same project type. For the engineering service providers that pass the initial screening, a second screening is performed based on the current order load, retaining only those with no order load. Then, a third screening is performed based on the comprehensive matching degree, extracting the engineering service provider with the maximum comprehensive matching degree as the target engineering service provider. If multiple maximum comprehensive matching degrees exist, a fourth screening is performed based on the quality standard matching degree, selecting the engineering service provider with the maximum quality standard matching degree as the target engineering service provider. If multiple maximum quality standard matching degrees exist, a fifth screening is performed based on the technical requirement matching degree, selecting the engineering service provider with the maximum technical requirement matching degree. If multiple sets of technical requirements match the target engineering service provider, a six-stage screening process is conducted based on the schedule matching degree to select the engineering service provider with the highest schedule matching degree. If multiple sets of schedule matching degrees match the target engineering service provider, a seven-stage screening process is conducted based on the material loss rate matching degree to select the engineering service provider with the highest material loss rate matching degree. If multiple sets of material loss rate matching degrees match the target engineering service provider, an eight-stage screening process is conducted based on the risk coefficient to select the engineering service provider with the highest risk coefficient matching degree. The order dispatch information is sent to the finally selected target engineering service provider. If there is no response within the time limit, the above screening process is automatically triggered again. After accepting the order, the order dispatch is immediately locked and the engineering service provider is notified, while the status information of all parties is updated.

[0026] The technical effects and advantages of this invention are as follows:

[0027] 1. To address the issue of weak concurrent processing capabilities in existing engineering project dispatching methods, this invention proposes an intelligent dispatching method based on AI-powered engineering project big data. By quantitatively analyzing the relationship between the number of associated engineering projects, the latest order processing time, and the urgency of the projects, the proposed dynamic priority adjustment mechanism can automatically optimize the order sorting of engineering projects based on their urgency, solving the problems of inability to judge urgency and weak concurrent processing capabilities in existing technologies. 2. To address the issue of excessively long project execution delays in existing engineering project dispatching methods, this invention monitors the order load of engineering service providers in real time, ensuring that engineering project orders are dispatched to service providers with no order load, thus solving the problem of dispatching engineering project orders to service providers with order loads and improving dispatching efficiency.

[0028] 2. To address the issue of incomplete quality assurance in existing project dispatching methods, this invention proposes a multi-level screening strategy. This strategy categorizes and filters various indicators. The target project service provider is selected based on the maximum comprehensive matching score. If multiple maximum comprehensive matching scores exist, screening is then performed based on the quality standard matching score, selecting the project service provider with the maximum quality standard matching score. If multiple maximum quality standard matching scores exist, screening is then performed based on the technical requirement matching score, selecting the project service provider with the maximum technical requirement matching score. If multiple maximum technical requirement matching scores exist, screening is then performed based on the schedule matching score. The system performs a screening process to select the engineering service provider with the highest project duration matching score as the target engineering service provider. If multiple sets of project duration matching scores are found, the system then filters based on material loss rate matching score, selecting the engineering service provider with the highest material loss rate matching score as the target engineering service provider. If multiple sets of material loss rate matching scores are found, the system then filters based on risk coefficient, selecting the engineering service provider with the highest risk coefficient matching score as the target engineering service provider. A dispatch message is then sent to the selected engineering service provider. If no response is received within the specified time, the above screening process is automatically triggered again. Upon receiving the order, the dispatch is immediately locked and the engineering service provider is notified, while simultaneously updating the status information of all parties. This improves the quality of project execution and the accuracy of matching. Attached Figure Description

[0029] Figure 1 This is a diagram illustrating the method steps of the present invention.

[0030] Figure 2 This is a system structure block diagram of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] like Figure 1 The embodiment shown provides an intelligent task dispatching method based on AI engineering project big data, including the following steps:

[0033] S1. Receive engineering project orders and identify key information about the engineering projects from various orders;

[0034] Furthermore, the project order information in step S1 includes basic information, core requirements, and auxiliary information. The basic information includes the project name, number, client name, and contact information; the core requirements include the project type, number of associated tasks, specific project location, expected processing time, latest order processing time, technical requirements, quality standards, and material loss rate; and the auxiliary information includes special requirements and acceptance specifications.

[0035] In this embodiment, it should be specifically noted that special requirements include terrain requirements, environmental constraints, security protocols, and confidentiality agreements, and acceptance specifications include acceptance methods, delivered materials, and acceptance standards.

[0036] S2. Obtain basic information, capability information, service information, and status information of the engineering service provider;

[0037] Furthermore, in step S2, the basic information of the engineering service provider includes the location and contact information of the engineering service provider; the capability information includes the qualification certificates and skill certificates of the engineering service provider; the service information is the historical engineering project record of the engineering service provider; the status information is the real-time order load of the engineering service provider; and the risk information is the number of legal disputes involving the engineering service provider as an individual or corporate entity in the past three years.

[0038] S3. Sort the engineering project orders in descending order according to their urgency, and assign the engineering project orders in the order of order.

[0039] Furthermore, the specific formula for calculating the urgency level X of the project order in step S3 is as follows: In the formula, Ma represents the number of associated engineering projects, Ts represents the latest order processing time, and the engineering project orders are sorted in descending order according to the calculated urgency of the engineering project orders, and the engineering project orders are assigned in order of order.

[0040] S4. Extract the engineering project order and engineering service provider's engineering feature data;

[0041] Furthermore, the engineering characteristic data for the project order in step S4 includes quality standards, technical requirements, processing time, and material loss rate; the engineering characteristic data for the engineering service provider includes qualification certificates, skill certificates, number of completed historical engineering projects, expected duration of historical engineering projects, actual duration of historical engineering projects, material loss rate of historical engineering projects, and the number of legal disputes involving the individual or corporate entity of the engineering service provider in the past three years.

[0042] S5. Based on the extracted engineering project orders and engineering service providers' engineering feature data, calculate the matching degree of quality standards, technical requirements, schedule, material loss rate, and risk between each engineering service provider and the engineering project order.

[0043] Furthermore, the steps for calculating the matching degree of each indicator in step S5 are as follows:

[0044] S51. Calculate the quality standard matching degree Sqi of the i-th engineering service provider. The specific calculation formula is as follows: In the formula, Nai is the number of historical engineering projects completed by the i-th engineering service provider, and Saij is the quality coefficient of the j-th historical engineering project of the i-th engineering service provider. When the quality standard of the j-th historical engineering project of the i-th engineering service provider meets the standard, the quality coefficient Saij = 1; when the quality standard of the j-th historical engineering project of the i-th engineering service provider does not meet the standard, the quality coefficient Saij = 0.

[0045] S52. Calculate the technical requirement matching degree Sti of the i-th engineering service provider. The calculation formula is: In the formula, Nmi is the number of technical requirements of the engineering project order that the i-th engineering service provider satisfies, and Nt is the total number of technical requirements of the engineering project order;

[0046] S53. Calculate the schedule matching degree Swi of the i-th project service provider. The calculation formula is: In the formula, Emin is the minimum value of the average historical project processing efficiency of the i-th engineering service provider, Emax is the maximum value of the average historical project processing efficiency of the i-th engineering service provider, and Eai is the average historical project processing efficiency of the i-th engineering service provider. The specific calculation formula is as follows: Eij represents the processing efficiency of the j-th historical project by the i-th engineering service provider, and the specific calculation formula is as follows: In the formula, Teij is the expected construction period of the j-th historical project of the i-th engineering service provider, and Taij is the actual construction period of the j-th historical project of the i-th engineering service provider.

[0047] S54. Calculate the material loss rate matching degree Sai for the i-th engineering service provider. The calculation formula is: In the formula, Fmin is the minimum average material loss rate of the historical projects of the i-th engineering service provider, Fmax is the maximum average material loss rate of the historical projects of the i-th engineering service provider, and Fai is the average material loss rate of the i-th engineering service provider. The calculation formula is as follows: Fij represents the material loss rate of the j-th historical engineering project of the i-th engineering service provider;

[0048] S55. Calculate the risk matching degree Sri of the i-th engineering service provider. The calculation formula is as follows: In the formula, Hai represents the number of legal disputes involving the i-th individual or corporate entity in the past three years, Hmin represents the minimum number of legal disputes involving all individual or corporate entities, and Hmax represents the maximum number of legal disputes involving all individual or corporate entities.

[0049] S6. Calculate the overall matching degree between each engineering service provider and the engineering project order based on the matching degree of quality standards, technical requirements, schedule, material loss rate, and risk, and generate a list of candidate engineering service providers.

[0050] Furthermore, in step S6, the comprehensive matching degree is calculated based on the matching degree of quality standards, technical requirements, construction period, material loss rate, and risk. For companies with historical project records, the specific calculation formula for the comprehensive matching degree Sc1 is as follows: For companies with no historical engineering project records, the formula for calculating the overall matching degree Sc2 is as follows: Based on the calculated overall matching degree, a list of engineering service providers is generated in descending order.

[0051] S7. Based on the list of candidate engineering service providers, generate target engineering service providers according to the preset screening rules and send order information. After the order is confirmed, lock the order. If the order is not accepted, regenerate the target engineering service provider and send the order.

[0052] Furthermore, in step S7, based on the list of candidate engineering service providers, a preliminary screening is conducted on the candidate service providers according to the project type of the engineering project orders, retaining only engineering service providers with the same engineering project type. For the engineering service providers that pass the preliminary screening, a second screening is conducted based on the current order load, retaining only those with no order load. Then, a third screening is conducted based on the comprehensive matching degree, extracting the engineering service provider corresponding to the maximum comprehensive matching degree as the target engineering service provider. If multiple maximum comprehensive matching degrees exist, a fourth screening is conducted based on the quality standard matching degree, selecting the engineering service provider corresponding to the maximum quality standard matching degree as the target engineering service provider. If multiple maximum quality standard matching degrees exist, a fifth screening is conducted based on the technical requirement matching degree, selecting the engineering service provider corresponding to the maximum technical requirement matching degree. If multiple sets of technical requirements match the target engineering service provider, a six-stage screening process is conducted based on the schedule matching degree to select the engineering service provider with the highest schedule matching degree. If multiple sets of schedule matching degrees match the target engineering service provider, a seven-stage screening process is conducted based on the material loss rate matching degree to select the engineering service provider with the highest material loss rate matching degree. If multiple sets of material loss rate matching degrees match the target engineering service provider, an eight-stage screening process is conducted based on the risk coefficient to select the engineering service provider with the highest risk coefficient matching degree. The order dispatch information is sent to the finally selected target engineering service provider. If there is no response within the time limit, the above screening process is automatically triggered again. After accepting the order, the order dispatch is immediately locked and the engineering service provider is notified, while the status information of all parties is updated.

[0053] In this embodiment, it is necessary to specifically explain the matching and filtering process between engineering project orders and engineering service providers, as shown below:

[0054] Set up a high-rise building construction project with a quality coefficient of 1, technical requirements: steel structure construction qualification, expected construction period of 180 days, and material wastage rate of ≤5%. There are 5 companies: A, B, C, D, and E. Company A undertakes high-rise building construction projects, holds a Class A steel structure construction qualification certificate, has 3 historical projects with quality coefficients of 1, 1, and 0 respectively, expected construction periods of 180 days, 90 days, and 200 days respectively, actual construction periods of 150 days, 75 days, and 220 days respectively, material wastage rates of 2%, 4%, and 6% respectively, and has 0 legal disputes. The current order load is 0.

[0055] Company B undertakes bridge construction projects. It holds a Class A steel structure construction qualification certificate. It has 4 historical projects with quality coefficients of 1, 1, 0, and 1 respectively. The expected construction periods for these projects were 150 days, 100 days, 200 days, and 50 days respectively, while the actual construction periods were 100 days, 75 days, 250 days, and 40 days respectively. Material wastage rates were 8%, 4%, 6%, and 6% respectively. The company has 2 legal disputes and a current order load of 1.

[0056] Company C undertakes high-rise building construction projects. Its steel structure construction qualification certificate is Level II. It has 2 historical projects with quality coefficients of 1 and 0 respectively. The expected construction period of the historical projects was 100 days and 50 days respectively, and the actual construction period of the historical projects was 80 days and 75 days respectively. The material loss rate was 8% and 6% respectively. It has 1 legal dispute and its current order load is 0.

[0057] Company D undertakes high-rise building construction projects. Its steel structure construction qualification certificate is Level 1. The number of historical projects is 0. There is no historical data on the compliance rate of historical project quality standards, the average construction period efficiency, the average material loss rate, the number of legal disputes, and the current order load is 0.

[0058] Company E undertakes high-rise building construction projects. It holds a Class A steel structure construction qualification certificate. The company has 4 historical projects with quality coefficients of 1, 1, 1, and 1 respectively. The expected construction periods for these projects were 150 days, 200 days, 200 days, and 60 days respectively, while the actual construction periods were 100 days, 160 days, 175 days, and 50 days respectively. The average material wastage rates were 5%, 2%, 2%, and 3% respectively. The company has 5 legal disputes and a current order load of 2.

[0059] Calculate the quality standard matching degree: A: SqA=2 / 3≈0.67, B: SqB=3 / 4=0.75, C: SqC=1 / 2=0.5, D: No historical data, E: SqE=4 / 4=1;

[0060] The calculation technology requires a matching degree, and all companies meet the steel structure qualification requirement, i.e., St=1.

[0061] Calculate the project schedule matching degree.

[0062] A: , , , , ;

[0063] B: , , , , , ;

[0064] C: , , , ;

[0065] D: Not applicable;

[0066] E: , , , , , ;

[0067] Material loss rate matching degree

[0068] A: Average material loss rate: , ;

[0069] B: Average material loss rate: , ;

[0070] C: Average material loss rate: , ;

[0071] D: Not applicable;

[0072] E: Average material loss rate: , ;

[0073] Calculate the risk matching degree.

[0074] A: B: C: D: E: ;

[0075] To calculate the overall matching degree, companies A, B, C, and E are calculated using Sc1, while company D is calculated using Sc2:

[0076] A: ;

[0077] B: ;

[0078] C: ;

[0079] D: ;

[0080] E: ;

[0081] Next, the system filters according to the preset filtering rules. Company B does not meet the criteria and is excluded. The system filters again. Company E has order load and is excluded. The system compares the overall matching degree and selects the target engineering service provider, Company D. The system sends the order information to Company D. If there is no response within the time limit, the system automatically triggers the above filtering process again and selects Company A as the target engineering service provider. If there is no response within the time limit, the system automatically triggers the above filtering process again and selects Company C as the target engineering service provider.

[0082] like Figure 2 This embodiment provides an intelligent order dispatching system based on AI-powered engineering project big data. It includes an engineering project order receiving module, an engineering service provider information acquisition module, an engineering project order sorting module, an engineering feature extraction module, an engineering feature matching degree analysis module, an engineering service provider candidate list generation module, a target engineering service provider screening module, an engineering project order dispatching module, and a database. The engineering project order receiving module is connected to the engineering project order sorting module. The engineering project order sorting module and the engineering service provider information acquisition module are connected to the engineering feature extraction module. The engineering feature extraction module, engineering feature matching degree analysis module, engineering service provider candidate list generation module, target engineering service provider screening module, and engineering project order dispatching module are sequentially connected. All modules in the system are connected to the database.

[0083] The project order receiving module receives project orders and identifies key information about the project.

[0084] The engineering service provider information acquisition module acquires the basic information, capability information, service information, status information, and risk information of the engineering service provider;

[0085] The project order sorting module sorts project orders in descending order based on their urgency and assigns them sequentially according to the order sequence.

[0086] The engineering feature extraction module extracts engineering feature data from engineering project orders and engineering service providers;

[0087] The engineering feature matching degree analysis module calculates the matching degree of quality standards, technical requirements, construction period, material loss rate, and risk between each engineering service provider and the engineering project order based on the extracted engineering project order and engineering feature data.

[0088] The engineering service provider candidate list generation module calculates the comprehensive matching degree between each engineering service provider and the engineering project order based on the matching degree of each engineering service provider with the quality standards, technical requirements, schedule, material loss rate, and risk, and generates a candidate engineering service provider list.

[0089] The target engineering service provider screening module filters the target engineering service providers according to the candidate engineering service provider list and the preset screening rules.

[0090] The project order assignment module sends order information to the target project service provider. Once the order is accepted, the order is locked. If the order is not accepted, a new target project service provider is generated for order assignment.

[0091] The database is used to store data information for all modules in the system.

[0092] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent task dispatching method based on AI engineering project big data, characterized in that: Includes the following steps: S1. Receive engineering project orders and identify key information about the engineering projects from various orders; S2. Obtain basic information, capability information, service information, status information, and risk information of the engineering service provider; S3. Sort the engineering project orders in descending order according to their urgency, and assign the engineering project orders in the order of order. S4. Extract the engineering project order and engineering service provider's engineering feature data; S5. Based on the extracted engineering project orders and engineering service providers' engineering feature data, calculate the matching degree of quality standards, technical requirements, schedule, material loss rate, and risk between each engineering service provider and the engineering project order. The matching degree calculation steps for each indicator in step S5 are as follows: S51. Calculate the quality standard matching degree Sqi of the i-th engineering service provider. The specific calculation formula is as follows: In the formula, Nai is the number of historical engineering projects completed by the i-th engineering service provider, and Saij is the quality coefficient of the j-th historical engineering project of the i-th engineering service provider. When the quality standard of the j-th historical engineering project of the i-th engineering service provider meets the standard, the quality coefficient Saij = 1; when the quality standard of the j-th historical engineering project of the i-th engineering service provider does not meet the standard, the quality coefficient Saij = 0. S52. Calculate the technical requirement matching degree Sti of the i-th engineering service provider. The calculation formula is: In the formula, Nmi is the number of technical requirements of the engineering project order that the i-th engineering service provider satisfies, and Nt is the total number of technical requirements of the engineering project order; S53. Calculate the schedule matching degree Swi of the i-th project service provider. The calculation formula is as follows: In the formula, Emin is the minimum value of the average historical project processing efficiency of the i-th engineering service provider, Emax is the maximum value of the average historical project processing efficiency of the i-th engineering service provider, and Eai is the average historical project processing efficiency of the i-th engineering service provider. The specific calculation formula is as follows: Eij represents the processing efficiency of the j-th historical project by the i-th engineering service provider, and the specific calculation formula is as follows: In the formula, Teij is the expected construction period of the j-th historical project of the i-th engineering service provider, and Taij is the actual construction period of the j-th historical project of the i-th engineering service provider. S54. Calculate the material loss rate matching degree Sai for the i-th engineering service provider. The calculation formula is: In the formula, Fmin is the minimum average material loss rate of the historical projects of the i-th engineering service provider, Fmax is the maximum average material loss rate of the historical projects of the i-th engineering service provider, and Fai is the average material loss rate of the i-th engineering service provider. The calculation formula is as follows: Fij represents the material loss rate of the j-th historical engineering project of the i-th engineering service provider; S55. Calculate the risk matching degree Sri of the i-th engineering service provider. The calculation formula is as follows: In the formula, Hai represents the number of legal disputes involving the i-th individual or corporate entity in the past three years, Hmin represents the minimum number of legal disputes involving all individual or corporate entities, and Hmax represents the maximum number of legal disputes involving all individual or corporate entities. S6. Calculate the overall matching degree between each engineering service provider and the engineering project order based on the matching degree of quality standards, technical requirements, schedule, material loss rate, and risk, and generate a list of candidate engineering service providers. S7. Based on the list of candidate engineering service providers, generate target engineering service providers according to the preset screening rules and send order information. After the order is accepted, lock the order. If the order is not accepted, regenerate the target engineering service provider and send the order.

2. The intelligent task dispatching method based on AI engineering project big data according to claim 1, characterized in that: In step S1, the key information of the project identified from various orders includes basic information, core requirements, and auxiliary information. Basic information includes project name, number, client name, and contact information; core requirements include project type, number of associated tasks, specific project location, expected processing time, latest order processing time, technical requirements, quality standards, and material loss rate; auxiliary information includes special requirements and acceptance specifications.

3. The intelligent task dispatching method based on AI engineering project big data according to claim 1, characterized in that: In step S2, the basic information of the engineering service provider includes the location and contact information of the engineering service provider; the capability information includes the qualification certificates and skill certificates of the engineering service provider; the service information is the historical engineering project record of the engineering service provider; the status information is the real-time order load of the engineering service provider; and the risk information is the number of legal disputes involving the engineering service provider as an individual or corporate entity in the past three years.

4. The intelligent task dispatching method based on AI engineering project big data according to claim 1, characterized in that: The specific formula for calculating the urgency level X of the project order in step S3 is as follows: In the formula, Ma represents the number of associated engineering projects, Ts represents the latest order processing time, and the engineering project orders are sorted in descending order according to the calculated urgency of the engineering project orders, and the engineering project orders are assigned in order of order.

5. The intelligent task dispatching method based on AI engineering project big data according to claim 1, characterized in that: In step S4, the engineering characteristic data of the engineering project order includes quality standards, technical requirements, processing time, and material loss rate; the engineering characteristic data of the engineering service provider includes qualification certificates, skill certificates, number of completed historical engineering projects, expected duration of historical engineering projects, actual duration of historical engineering projects, material loss rate of historical engineering projects, and the number of legal disputes involving the individual or corporate entity of the engineering service provider in the past three years.

6. The intelligent task dispatching method based on AI engineering project big data according to claim 1, characterized in that: In step S6, a comprehensive matching degree is calculated based on the matching degree of quality standards, technical requirements, construction period, material loss rate, and risk. For companies with historical project records, the specific formula for calculating the comprehensive matching degree Sc1 is as follows: For companies with no historical engineering project records, the formula for calculating the overall matching degree Sc2 is as follows: Based on the calculated overall matching degree, a list of engineering service providers is generated in descending order.

7. The intelligent task dispatching method based on AI engineering project big data according to claim 1, characterized in that: In step S7, based on the candidate engineering service provider list, preliminary screening is performed on the candidate service providers according to the project type of the engineering project orders, retaining only engineering service providers with the same engineering project type. For the engineering service providers that pass the preliminary screening, a second screening is performed based on the current order load, retaining only those with no order load. Then, a third screening is performed based on the comprehensive matching degree, extracting the engineering service provider corresponding to the maximum comprehensive matching degree as the target engineering service provider. If multiple sets of maximum comprehensive matching degree exist, a fourth screening is performed based on the quality standard matching degree, selecting the engineering service provider corresponding to the maximum quality standard matching degree as the target engineering service provider. If multiple sets of maximum quality standard matching degree exist, a fifth screening is performed based on the technical requirement matching degree, selecting the engineering service provider corresponding to the maximum technical requirement matching degree. If multiple sets of technical requirements match the target project service provider, a six-stage screening process is conducted based on the project schedule matching degree to select the project service provider with the highest project schedule matching degree as the target project service provider. If multiple sets of project schedule matching degrees match the target project service provider, a seven-stage screening process is conducted based on the material loss rate matching degree to select the project service provider with the highest material loss rate matching degree as the target project service provider. If multiple sets of material loss rate matching degrees match the target project service provider, an eight-stage screening process is conducted based on the risk coefficient to select the project service provider with the highest risk coefficient as the target project service provider. The order dispatch information is sent to the finally selected target project service provider. If there is no response within the time limit, the above screening process is automatically triggered again. After accepting the order, the order dispatch is immediately locked and the project service provider is notified, while the status information of all parties is updated.

Images