Automatic order distribution method for detection-type orders, management platform, storage medium and equipment

Abstract

The invention belongs to the technical field of material detection and order management, and relates to an automatic order distribution method for detection orders, a management platform, a storage medium and equipment, and the method comprises the steps: order preprocessing, order distribution rule presetting, order distribution according to the rule, order distribution result recording and order tracking. According to the method, a user demand order is converted into a standardized experiment list, project information in the experiment list and equipment standardized performance description of each laboratory are mapped, an available laboratory list is screened out, historical data of the laboratory are combined, factors such as a predicted experiment period, express delivery duration, a gross profit rate and laboratory scores are comprehensively considered, and the experiment efficiency is improved. A weighted total value of each laboratory is obtained through a weighting algorithm, and a user demand order is automatically and seamlessly docked to a selected target laboratory by combining an order distribution funnel and obtaining an optimal order dispatching strategy, so that efficient and accurate docking of a service supplier and a service demander is ensured, and the optimal balance of improving order delivery efficiency and delivery quality and improving profit rate is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of material testing and order management, and in particular to an automatic order splitting method, management platform, storage medium and equipment for testing orders. Background Art

[0002] In the current order allocation practice in the field of sample testing, the traditional manual allocation mechanism relies significantly on individual subjective judgment and experience. Although this model has its flexibility, it inevitably limits the automation of the allocation process and the optimization of scale benefits. Specifically, manual allocation often finds it difficult to transcend the limitations of a local perspective, and it is difficult to achieve accurate matching and efficient scheduling of global resources, which in turn leads to low resource utilization efficiency and non-optimal order cost configuration.

[0003] The core of the existing problem is that the resource scheduling technology has a low degree of automation and over-reliance on manual operations for resource matching, which not only hinders the implementation of the global optimization strategy, but also directly manifests itself in the following aspects: laboratories on the service supply side face a polarized dilemma in resource allocation; on the one hand, cost-effective resources such as self-operated laboratories are not fully utilized, resulting in the loss of potential cost-saving opportunities; on the other hand, some non-self-operated laboratories are overloaded due to task concentration, affecting service quality and efficiency, while the remaining non-self-operated laboratories have idle resources due to insufficient order allocation, resulting in resource waste. This unbalanced state not only weakens the company's cost control ability and overall profitability, but may also have an adverse impact on customer service response speed, test quality and the final user experience. Therefore, there is an urgent need for a more intelligent and adaptively optimized order allocation system to achieve maximum resource utilization and precise management of supply and demand balance. Based on this, it is very necessary to develop an automatic order splitting method for testing orders. Summary of the invention

[0004] In view of the problems of excessive laboratory load and idle resources coexisting when allocating existing testing orders, as well as suboptimal configuration of order costs, the present invention aims to provide a method for automatic order splitting of testing orders. In combination with the characteristics of testing orders, the method comprehensively considers factors such as the expected experimental cycle, delivery time, gross profit margin and laboratory score of each available laboratory, and combines with an order splitting funnel to provide an intelligent and automated order splitting model to efficiently and accurately allocate material testing orders, so as to improve corporate profit margins, testing efficiency and customer satisfaction.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a method for automatically splitting detection orders, comprising the following steps: Step 1: Order pre-processing: When receiving a user's demand order, the user's demand order is converted into a standardized experiment order according to a standardized experiment order template; Step 2: Preset order splitting rules: According to the test items and test conditions of the samples to be tested in the standardized test order, a list of available laboratories is screened out; according to the laboratory's historical test data, the expected experimental cycle, gross profit margin and laboratory score of the laboratory executing the corresponding standardized test order are calculated, and the express delivery time from the place of shipment of the samples to be tested in the standardized test order to the laboratory is calculated, and the weighted total value P of each laboratory is obtained through a weighted algorithm; the optimal laboratory obtained by screening the weighted total value P is output as the order object; Step 3: Allocate orders according to the rules: First, determine whether the user specifies a target laboratory. If the target laboratory is specified, directly output the corresponding target laboratory as the order object; If the user does not specify a target laboratory, the most suitable target laboratory will be selected according to the order splitting rules in step 2 as the order splitting object output; Step 4: Record the order splitting results: Record user demand orders and laboratory order splitting results, and store the order splitting results in the database; Step 5: Track your order: Track the execution progress of user demand orders and provide feedback on order splitting status.

[0006] The present invention standardizes the user's demand orders, maps each specific test demand indicator in the user's demand orders, such as test items and test conditions, with the standardized performance description of the equipment in each laboratory, and screens out a list of available laboratories. Based on the laboratory's historical data, the present invention comprehensively considers factors such as the expected experimental cycle, delivery time, gross profit margin, and laboratory score, and obtains the weighted total value of each laboratory through a weighted algorithm, thereby obtaining a laboratory that matches the user's demand order, ensuring efficient and accurate docking between the service supply and demand parties, optimizing resource allocation, and improving the delivery quality and efficiency of testing orders.

[0007] Furthermore, the calculation formula of the weighted total value P is as follows: P=a×(1−x / S1)+b×(1−w / S2)+c×y / S3+d×z / S4; Where P is the weighted total value; x is the actual value of the expected experimental period, S1 is the standard value of the expected experimental period; a is the weighting coefficient of the expected experimental period, 0.25≤a≤0.35; w is the actual value of the delivery time, S2 is the standard value of the delivery time, b is the weighting coefficient of the delivery time, 0.05≤b≤0.15; y is the actual value of gross profit margin, S3 is the standard value of gross profit margin, c is the weighted coefficient of gross profit margin, 0.35≤c≤0.45; z is the actual value of the laboratory score, S4 is the standard value of the laboratory score, and d is the weighting coefficient of the laboratory score, 0.15≤d≤0.25; And the weighting coefficients a, b, c, d satisfy: a+b+c+d=1.

[0008] Assign corresponding weighting coefficients according to the estimated experimental cycle, delivery time, gross profit margin and the importance of laboratory scores, and determine the weighted total value in combination with a specific weighting algorithm. This makes the weighted total value more in line with the expectations of the order splitting results, improves the accuracy of the order splitting results, and effectively improves the order delivery efficiency and delivery quality, while reducing order costs and increasing profitability.

[0009] Furthermore, the order splitting rules preset in step 2 also include an order splitting funnel, which is as follows: Condition 1: laboratory score ≥80; Condition 2: The top 50% laboratories in order of order cycle from shortest to longest; Condition 3: The top 75% of laboratories in order of order cycle from shortest to longest; The laboratories are divided into self-operated laboratories and non-self-operated laboratories according to their nature; the order cycle is the sum of the experimental cycle and the delivery time; a. When the number of laboratories that meet both conditions 1 and 2 is greater than 0; If there is a self-operated laboratory, the order will be distributed to the nearest self-operated laboratory; If there is no self-operated laboratory, compare the weighted total values ​​of these non-self-operated laboratories and give the order to the non-self-operated laboratory with the highest weighted total value; b. When the number of laboratories that meet conditions 1 and 2 at the same time = 0, and the number of laboratories that meet conditions 1 and 3 at the same time > 0; If there is a self-operated laboratory, it will be distributed to the nearest self-operated laboratory; If there is no self-operated laboratory, compare the weighted total values ​​of these non-self-operated laboratories and give the order to the laboratory with the highest weighted total value; c. When the number of laboratories that meet the above conditions 1 and 3 = 0; Compare the weighted total values ​​of all laboratories that can accept the order, and assign the order to the laboratory with the highest weighted total value.

[0010] Laboratories are divided into self-operated laboratories and non-self-operated laboratories based on their nature. For the same testing project, self-operated laboratories have higher gross profit margins than non-self-operated laboratories. The order splitting funnel can be used to further optimize the order splitting plan to ensure that the profits corresponding to the order splitting results are maximized.

[0011] Furthermore, the laboratory score includes a service attitude score, a service efficiency score, and a customer's quality score for the laboratory test results. The calculation formula for the laboratory score is as follows: Laboratory score = e × service attitude score + f × service efficiency score + g × quality score; among which, e, f, and g meet the following conditions: 0.15≤e≤ 0.25, 0.25≤f≤0.35, 0.45≤g≤0.55; e+f+g=1.

[0012] Based on the historical data of laboratory ratings, we analyzed the influencing factors and determined the key factors affecting laboratory ratings, such as service attitude, service efficiency, and the quality of test results. We found that the quality of test results and the timeliness of tests are relatively more important to users. By allocating coefficients to the importance of factors affecting laboratory ratings, we made the laboratory ratings more closely matched with user needs. This way, we can improve the quality of test data and user satisfaction for order delivery while ensuring delivery efficiency and high profitability.

[0013] Furthermore, the gross profit margin depends on the nature of the laboratory. Laboratories are divided into self-operated laboratories and non-self-operated laboratories according to their nature. If the laboratory is a self-operated laboratory, the gross profit margin is defined as 100%; if the laboratory is a non-self-operated laboratory, the gross profit margin is calculated according to the following formula: Gross profit margin = (charges - costs) / charges * 100%.

[0014] Furthermore, when there are multiple laboratories with the highest and equal weighted total values, orders will be given priority to the laboratory with the highest gross profit margin.

[0015] When there are multiple target laboratories screened out by the order splitting funnel combined with the weighted total value, and their weighted total values ​​are the highest and equal, the laboratory with the highest gross profit margin for the testing items of the order is selected to ensure the maximum profit margin for the order.

[0016] Furthermore, the screening factors for selecting the available laboratory list in step 2 include the laboratory weekly capacity, which is the number of orders that the laboratory can complete each week. When the laboratory weekly capacity is saturated, the laboratory is marked as a saturated state and no longer participates in order allocation, and is not written into the available laboratory list.

[0017] During the initial screening of the laboratory, the weekly capacity of the laboratory is taken into consideration. Although the laboratory has a high historical delivery efficiency, once its weekly capacity is saturated, the delivery efficiency of subsequent orders will be reduced. Therefore, whether the laboratory can accept new orders is determined based on whether its weekly capacity is saturated. When the laboratory's weekly capacity is saturated, it will no longer participate in order allocation in the current period to ensure the delivery efficiency of orders.

[0018] The weekly capacity of the laboratory is dynamically changing data. If the available laboratories for the order are saturated in the current cycle but may not be saturated in the next cycle, they will be included in the list of available laboratories.

[0019] In a second aspect, the present invention provides an automatic order splitting management platform for testing orders, the management platform is constructed based on the above-mentioned automatic order splitting method, and includes an order entry module, a standardization module, an order dispatching engine module, an allocation module, a laboratory receipt confirmation module, a storage module and a query tracking module; The order entry module provides an order entry port, through which users enter order information into the system; The standardization module is used to generate a standardized experiment sheet according to the standardized experiment sheet template based on the order information entered by the user; The dispatch engine module determines the optimal dispatch plan based on the standardized information provided by the standardized experiment sheet and the dispatch rules, and outputs the dispatch results. The allocation module is used to receive the order splitting results and allocate the corresponding orders to the corresponding laboratories according to the order splitting results; The laboratory receipt confirmation module is used to confirm the receipt of the order results; The storage module records and stores order information, order splitting information and order splitting confirmation results through logs; The query tracking module is used to query order information, order progress and order track.

[0020] In a third aspect, the present invention provides a computer-readable storage medium, wherein the computer storage medium stores a computer program, and when the computer program is run, the above-mentioned automatic order splitting method is executed.

[0021] In a fourth aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory is used to store program instructions; the processor is used to call the program instructions stored in the memory, and execute the above-mentioned automatic order splitting method according to the obtained program.

[0022] The beneficial effects of the above technical solution are as follows: 1. Improve order processing efficiency Automated allocation: Through preset order distribution rules and algorithms, the system can automatically allocate orders to the most suitable laboratory or inspector without manual intervention, greatly improving the efficiency of order processing.

[0023] Reduce waiting time: The automated allocation mechanism ensures that orders are processed in a timely manner, reducing the time customers have to wait for test results and improving customer satisfaction.

[0024] 2. Optimize resource allocation Load balancing: The system can take into account factors such as the busy or idle status of the laboratory and the availability of equipment to achieve load balancing and avoid situations where some laboratories are overloaded while other resources are idle.

[0025] Resource matching: Based on the specific requirements of the order, the system can match the most suitable laboratory to ensure the accuracy and reliability of the test results.

[0026] 3. Improve customer satisfaction Quick response: The automated order allocation mechanism enables the system to respond quickly to customer needs and provide timely testing services.

[0027] Transparent management: The system can update the order status in real time for customers or administrators to query, which improves management transparency and customer trust.

[0028] 4. Enhance enterprise competitiveness Improve service quality: By automating the order allocation and inspection process, companies can provide more efficient, accurate and reliable services, thereby improving the overall service quality.

[0029] Reduced costs: Automated allocation mechanisms reduce labor costs and error costs, helping companies reduce costs and improve profitability.

[0030] In summary, the present invention screens and analyzes the influencing factors of the order splitting of testing orders, matches the corresponding weighted coefficients according to the importance of the influencing factors, combines the historical data of each laboratory, obtains the weighted total value of each available laboratory, and combines the order splitting funnel to automatically, efficiently and accurately allocate material testing orders to the corresponding laboratories, optimize resource allocation, and comprehensively achieve the best balance between improving order delivery efficiency, delivery quality and improving profit margins. It has broad application prospects and high promotion value. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a logic block diagram of the present invention; Figure 2 This is a flow chart of dispatching orders according to the present invention; Figure 3 This is a flow chart of automatic order splitting of the present invention. DETAILED DESCRIPTION

[0032] The following is combined with Figures 1 to 3 The present invention is further described in detail with reference to the accompanying drawings and specific embodiments.

[0033] Example 1 This embodiment takes the automatic order splitting method of material testing orders as an example to explain in detail the technical solution of the present application. Through intelligent and automated methods, material testing orders are efficiently and accurately allocated to achieve the goal of comprehensively improving corporate profit margins, testing efficiency and customer satisfaction.

[0034] This embodiment provides a method for automatically splitting detection orders, such as Figures 1 to 3 As shown in, the following steps are included; Step 1: Order pre-processing: When receiving a user's demand order, the user's demand order is converted into a standardized experiment form according to the standardized experiment form template; the standardized experiment form includes the sample delivery number, project information and address; the sample delivery number is the order number; the project information is the detailed requirements of the order, including sample name, sample status, sample size, hazard, storage requirements, sample quantity, sample number, sample composition, test conditions and requirements, and remarks; the address includes the shipping place of the samples to be tested and the location of the laboratory.

[0035] Step 2: Preset order splitting rules: According to the test item information of the samples to be tested in the standardized test order, the list of available laboratories is screened out. When screening available laboratories, it is necessary to consider whether the weekly capacity of the laboratory has reached saturation. The weekly capacity of the laboratory is the number of orders that the laboratory can complete each week. When the weekly capacity of the laboratory with corresponding test equipment and testing capabilities is saturated, the laboratory is marked as no longer participating in order allocation and is not written into the list of available laboratories. Since the weekly capacity of the laboratory is a dynamically changing data, when it is not in a saturated state and the laboratory can detect the corresponding items in the demand order, it can be included in the list of available laboratories.

[0036] According to the laboratory's historical testing data, the laboratory's estimated experimental cycle, gross profit margin and laboratory score for executing the corresponding standardized experimental order are calculated, and the express delivery time from the place of shipment of the samples to be tested in the standardized experimental order to the laboratory is calculated. The weighted total value P of each laboratory is obtained through a weighted algorithm; the optimal laboratory obtained by screening the weighted total value P is output as the order object.

[0037] During implementation, the expected experimental cycle, gross profit margin and laboratory score of the laboratory executing the corresponding experimental order are stored in the project with project information as the classification standard based on historical data, and the address of the laboratory is stored in the project with address as the classification standard.

[0038] In specific implementation, the calculation formula of the weighted total value P is as follows: P=a×(1−x / S1)+b×(1−w / S2)+c×y / S3+d×z / S4; Where P is the weighted total value; x is the actual value of the expected experimental period, S1 is the standard value of the expected experimental period; a is the weighting coefficient of the expected experimental period, where 0.25≤a≤0.35; w is the actual value of the delivery time, S2 is the standard value of the delivery time, and b is the weighting coefficient of the delivery time, where 0.05≤b≤0.15; y is the actual value of gross profit margin, S3 is the standard value of gross profit margin, and c is the weighted coefficient of gross profit margin, where 0.35≤c≤0.45; z is the actual value of the laboratory score, S4 is the standard value of the laboratory score, and d is the weighting coefficient of the laboratory score, where 0.15≤d≤0.25; And the weighting coefficients a, b, c, d satisfy: a+b+c+d=1.

[0039] The laboratory score includes the service attitude score, service efficiency score, and the customer's quality score for the laboratory test results. The calculation formula for the laboratory score is as follows: Laboratory score = e × service attitude score + f × service efficiency score + g × quality score; among which, e, f, and g meet the following conditions: 0.15≤e≤ 0.25, 0.25≤f≤0.35, 0.45≤g≤0.55; e+f+g=1.

[0040] Step 3: Allocate orders according to the rules: First, determine whether the user specifies a target laboratory. If the target laboratory is specified, directly output the corresponding target laboratory as the order object; If the user does not specify a target laboratory, the most suitable target laboratory will be selected as the order object according to the order distribution rules in step 2. When there are multiple laboratories with the highest and equal weighted total values, the order will be distributed to the laboratory with the highest gross profit margin first.

[0041] During implementation, this embodiment will determine how to handle the experimental order based on "whether the equipment is specified". If the customer specifies the equipment, the process may jump directly to the corresponding dispatching step and distribute the order to the target laboratory; if it is not specified, it will enter the evaluation stage of the "intelligent dispatching engine". In the "intelligent dispatching engine", the system will comprehensively consider multiple factors, optimize the dispatching strategy through the total weighted value of each laboratory, and screen out the target laboratories for dispatching. Factors considered include "estimated test cycle", "express delivery time", "gross profit margin", and "laboratory score", etc. These factors reflect the efficiency, cost and delivery quality of different laboratories when executing experimental orders. In addition, during the screening stage of available laboratories, the system will also consider whether the "weekly capacity" of the laboratory is saturated to evaluate the laboratory's acceptance capacity and delivery efficiency.

[0042] Step 4: Record the order splitting results: Record user demand orders and laboratory order splitting results, and store the order splitting results in the database; Step 5: Track your order: Track the execution progress of user demand orders and provide feedback on order splitting status.

[0043] In this embodiment, upon receiving a user's demand order, the technical consultant forms a standardized lab sheet according to the standardized lab sheet template, and maps each specific demand indicator in the user's demand order with the standardized performance description of the equipment in each available laboratory, aiming to identify at least one laboratory that can perform sample testing for the user's demand order, and form a list of available laboratories. This process relies on a standardized form template, which is derived from a demand corpus constructed after an in-depth analysis of a detailed data set of historical service orders. Furthermore, the standardized capability description of the laboratory is formed by intelligently integrating its unstructured technical capability description with the aforementioned demand corpus.

[0044] On this basis, we use a certain screening algorithm, combined with multiple information such as the attribute characteristics and real-time operation status of each candidate laboratory, to conduct a comprehensive evaluation of these candidate laboratories, obtain the optimal dispatching strategy, and accurately locate the most suitable target laboratory. Subsequently, the user's demand order is seamlessly connected to the selected target laboratory, ensuring efficient and accurate connection between the service supply and demand parties, optimizing resource allocation, and improving the quality and efficiency of order delivery.

[0045] Example 2 This embodiment further optimizes the preset order splitting rules on the basis of embodiment 1, not only considering the influence of the single condition of weighted total value on the screening and determination of the target laboratory, but also performing order splitting funnel based on the weighted total value, further optimizing the screening strategy of the target laboratory, and the rest of the parts not described are the same as embodiment 1, and the details of this embodiment are as follows: In this embodiment, based on the nature of the laboratory, the laboratory is divided into self-operated laboratories and non-self-operated laboratories. The gross profit margin is determined according to the nature of the laboratory. If the laboratory is a self-operated laboratory, the gross profit margin is defined as 100%; if the laboratory is a non-self-operated laboratory, the gross profit margin is calculated according to the following formula: gross profit margin = (charges-costs) / charges*100%. The gross profit margin in Example 1 can also be defined in this way.

[0046] In view of the different classifications of the above laboratories, when the intelligent dispatching engine optimizes the dispatching strategy, it also performs order splitting funnel processing on the basis of the weighted total value in order to screen out target laboratories with higher matching degree. The specific steps are as follows; The order splitting funnel is as follows: Condition 1: laboratory score ≥80; Condition 2: The top 50% laboratories in order of order cycle from shortest to longest; Condition 3: The top 75% of laboratories in order of order cycle from shortest to longest; Among them, the order cycle is the sum of the experimental cycle and the delivery time; a. When the number of laboratories that meet both conditions 1 and 2 is greater than 0; If there is a self-operated laboratory, the order will be distributed to the nearest self-operated laboratory; If there is no self-operated laboratory, compare the weighted total values ​​of these non-self-operated laboratories and give the order to the non-self-operated laboratory with the highest weighted total value; b. When the number of laboratories that meet conditions 1 and 2 at the same time = 0, and the number of laboratories that meet conditions 1 and 3 at the same time > 0; If there is a self-operated laboratory, it will be distributed to the nearest self-operated laboratory; If there is no self-operated laboratory, compare the weighted total values ​​of these non-self-operated laboratories and give the order to the laboratory with the highest weighted total value; c. When the number of laboratories that meet the above conditions 1 and 3 = 0; Compare the weighted total values ​​of all laboratories that can accept the order, and assign the order to the laboratory with the highest weighted total value.

[0047] When there is more than one laboratory with the highest and equal weighted total value selected from the above three situations a, b, and c, the laboratory with the highest gross profit margin will be given priority as the target laboratory.

[0048] During the evaluation process, this embodiment adjusts the weight according to the nature of the laboratory (self-operated or non-self-operated). For self-operated laboratories, the system mainly considers the weight of the "estimated experimental cycle" to ensure that the experiment can be completed on time; for non-self-operated laboratories, the system will comprehensively consider multiple factors such as "gross profit margin", "express delivery time", "laboratory score" and "estimated experimental cycle" to find the balance between cost-effectiveness and efficiency and delivery quality. Ultimately, through a rule-based dispatch algorithm, weighted analysis and order distribution funnel, the system will determine an optimal dispatch strategy to dispatch user orders to highly matched target laboratories, aiming to ensure the best balance between the quality of experimental results, cost-effectiveness and order delivery efficiency.

[0049] This example demonstrates a complex and sophisticated dispatching system that takes multiple factors into consideration to ensure that the lab orders are optimally assigned to the appropriate labs, thereby achieving the best balance between cost-effectiveness, efficiency, and quality. The present embodiment provides an automatic order distribution management platform for testing orders, including an order entry module, a standardization module, an order distribution engine module, an allocation module, a laboratory reception confirmation module, a storage module and a query tracking module; the order entry module provides an order entry port, and the user enters the order information into the system through the order entry port; the standardization module is used to form a standardized experiment form according to the standardized experiment form template based on the order information entered by the user; the order distribution engine module determines the optimal order distribution plan based on the standardized information provided by the standardized experiment form and the order distribution rules, and outputs the order distribution results; the allocation module is used to receive the order distribution results, and distribute the corresponding order to the corresponding laboratory according to the order distribution results; the laboratory reception confirmation module is used to confirm the receipt of the order distribution results; the storage module records and stores the order information, order distribution information and order distribution confirmation results through logs; the query tracking module is used to query the order information, order progress and order track.

[0050] For the above management platform, when using it, Figure 2 The flowchart shown in the figure shows the complete process from customer order placement to automatic dispatch of the order to the target laboratory. The process is divided into several key steps: 1. Customer places an order: This is the starting point of the entire process. The customer submits the order through the order entry module and proposes the testing requirements of the sample.

[0051] 2. Issue a standard test sheet for an order: After the system receives an order from a customer, the standardization module generates a standard test sheet based on the customer's order content. This standard test sheet lists in detail the specific information of the sample and the test conditions of the sample, providing a basis for subsequent processing.

[0052] 3. Intelligent dispatch engine: Finally, the dispatch engine module will intervene. This engine uses algorithms and rules to optimize the standard experimental orders, considering multiple factors (such as the weekly capacity of the laboratory, the experimental cycle, the delivery time of customer samples to the laboratory, gross profit margin, laboratory rating, etc.) to find the most suitable dispatch solution.

[0053] 4. Obtain the optimal dispatch strategy: Through the analysis and calculation of the intelligent dispatch engine, the system will obtain an optimal dispatch strategy and screen out the target laboratories. This strategy takes into account multiple factors and aims to achieve the optimal balance between test efficiency, cost and test quality.

[0054] 5. Orders are automatically dispatched to target laboratories: The system will automatically dispatch orders to selected target laboratories through the allocation module based on the optimal dispatching strategy. This process is automated, reducing manual intervention and improving processing speed and accuracy.

[0055] This embodiment provides a complete process from customer order placement to automatic order dispatch to service providers, and through the intervention of the dispatch engine, realizes automated processing and optimized dispatch of orders.

[0056] If the module in this embodiment is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium or a computer device. Specifically, a computer device includes: a memory for storing program instructions; a processor for calling the program instructions stored in the memory, and executing the automatic order splitting method in Example 1 and Example 2 according to the obtained program.

[0057] Based on such understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium. The computer storage medium stores a computer program. When the computer program is run, the automatic order splitting method in Example 1 is executed. The computer-readable storage medium can be a non-volatile computer-readable storage medium. The computer-readable storage medium stores instructions. When the instructions are run on a computer, the computer executes the automatic order splitting method in Example 1. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc. Various media that can store program codes.

[0058] The embodiments of the present invention described above do not constitute a limitation on the protection scope of the present invention. The basic concept of the present invention is to solve the problem of laboratory order allocation in an intelligent and automated manner, combined with the characteristics of laboratory testing orders, comprehensively consider the expected experimental cycle, delivery time, gross profit margin and score, and provide a screening and dispatching model to efficiently and accurately allocate material testing orders to improve corporate profit margins, testing efficiency and customer satisfaction. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for automatically splitting detection orders, characterized in that: The steps include: Step 1: Order pre-processing: When receiving a user's demand order, the user's demand order is converted into a standardized experiment order according to a standardized experiment order template; Step 2: Preset order splitting rules: According to the test items and test conditions of the samples to be tested in the standardized test order, a list of available laboratories is screened out; according to the laboratory's historical test data, the expected experimental cycle, gross profit margin and laboratory score of the laboratory executing the corresponding standardized test order are calculated, and the express delivery time from the place of shipment of the samples to be tested in the standardized test order to the laboratory is calculated, and the weighted total value of each laboratory is obtained through a weighted algorithm; the optimal laboratory obtained by screening the weighted total value is output as the order object; Step 3: Allocate orders according to the rules: First, determine whether the user specifies a target laboratory. If the target laboratory is specified, directly output the corresponding target laboratory as the order object; If the user does not specify a target laboratory, the most suitable target laboratory will be selected according to the order splitting rules in step 2 as the order splitting object output; Step 4: Record the order splitting results: Record user demand orders and laboratory order splitting results, and store the order splitting results in the database; Step 5: Track your order: Track the execution progress of user demand orders and provide feedback on order splitting status.

2. The automatic order splitting method for detection orders according to claim 1 is characterized in that: The weighted total value is calculated as follows: P=a×(1−x / S1)+b×(1−w / S2)+c×y / S3+d×z / S4; Where P is the weighted total value; x is the actual value of the expected experimental period, S1 is the standard value of the expected experimental period; a is the weighting coefficient of the expected experimental period, 0.25≤a≤0.35; w is the actual value of the delivery time, S2 is the standard value of the delivery time, b is the weighting coefficient of the delivery time, 0.05≤b≤0.15; y is the actual value of gross profit margin, S3 is the standard value of gross profit margin, c is the weighted coefficient of gross profit margin, 0.35≤c≤0.45; z is the actual value of the laboratory score, S4 is the standard value of the laboratory score, and d is the weighting coefficient of the laboratory score, 0.15≤d≤0.25; And the weighting coefficients a, b, c, d satisfy: a+b+c+d=1.

3. The automatic order splitting method for detection orders according to claim 1 is characterized in that: The preset order splitting rules in step 2 also include an order splitting funnel, which is as follows: Condition 1: laboratory score ≥80; Condition 2: The top 50% laboratories in order of order cycle from shortest to longest; Condition 3: The top 75% of laboratories in order of order cycle from shortest to longest; The laboratories are divided into self-operated laboratories and non-self-operated laboratories according to their nature; the order cycle is the sum of the experimental cycle and the delivery time; a. When the number of laboratories that meet both conditions 1 and 2 is greater than 0; If there is a self-operated laboratory, the order will be distributed to the nearest self-operated laboratory; If there is no self-operated laboratory, compare the weighted total values ​​of these non-self-operated laboratories and give the order to the non-self-operated laboratory with the highest weighted total value; b. When the number of laboratories that meet conditions 1 and 2 at the same time = 0, and the number of laboratories that meet conditions 1 and 3 at the same time > 0; If there is a self-operated laboratory, it will be distributed to the nearest self-operated laboratory; If there is no self-operated laboratory, compare the weighted total values ​​of these non-self-operated laboratories and give the order to the laboratory with the highest weighted total value; c. When the number of laboratories that meet the above conditions 1 and 3 = 0; Compare the weighted total values ​​of all laboratories that can accept the order, and assign the order to the laboratory with the highest weighted total value.

4. The automatic order splitting method for detection orders according to claim 1 is characterized in that: The laboratory score includes the service attitude score, service efficiency score, and the customer's quality score for the laboratory test results. The calculation formula for the laboratory score is as follows: Laboratory score = e × service attitude score + f × service efficiency score + g × quality score; among which, e, f, and g meet the following conditions: 0.15≤e≤ 0.25, 0.25≤f≤0.35, 0.45≤g≤0.55; e+f+g=1.

5. The automatic order splitting method for detection orders according to claim 2 is characterized in that: The gross profit margin is determined based on the nature of the laboratory. Laboratories are divided into self-operated laboratories and non-self-operated laboratories based on their nature. If the laboratory is a self-operated laboratory, the gross profit margin is defined as 100%; if the laboratory is a non-self-operated laboratory, the gross profit margin is calculated according to the following formula: Gross profit margin = (charges - costs) / charges * 100%.

6. The automatic order splitting method for detection orders according to claim 3 is characterized in that: When there are multiple laboratories with the highest and equal weighted total values, orders will be given priority to the laboratory with the highest gross profit margin.

7. The automatic order splitting method for detection orders according to claim 1 is characterized in that: The screening factors for selecting the available laboratory list in step 2 include the laboratory weekly capacity, which is the number of orders that the laboratory can complete each week. When the laboratory weekly capacity is saturated, the laboratory is marked as a saturated state and no longer participates in order allocation, and is not written into the available laboratory list.

8. An automatic order distribution management platform for testing orders, characterized in that: The management platform is constructed based on the automatic order distribution method according to any one of claims 1 to 7, and includes an order entry module, a standardization module, an order dispatch engine module, an allocation module, a laboratory receipt confirmation module, a storage module and a query tracking module; The order entry module provides an order entry port, through which the user enters order information into the system; The standardization module is used to generate a standardized experiment sheet according to the standardized experiment sheet template based on the order information entered by the user; The dispatch engine module determines the optimal order splitting scheme based on the standardized information provided by the standardized experiment sheet and the order splitting rules, and outputs the order splitting results; The allocation module is used to receive the order splitting result and allocate the corresponding order to the corresponding laboratory according to the order splitting result; The laboratory receiving confirmation module is used to confirm the receipt of the order splitting results; The storage module records and stores order information, order splitting information and order splitting confirmation results through logs; The query tracking module is used to query order information, order progress and order track.

9. A computer-readable storage medium, characterized in that: The computer storage medium stores a computer program, and when the computer program is executed, the automatic order splitting method as described in any one of claims 1 to 7 is executed.

10. A computer device, characterized in that: It comprises a memory and a processor; the memory is used to store program instructions; the processor is used to call the program instructions stored in the memory, and execute the automatic order splitting method as described in any one of claims 1 to 7 according to the obtained program.

Images