Dynamic resource data distribution method and device based on data processing

Abstract

The invention discloses a dynamic resource allocation method and device based on data processing, and belongs to the technical field of data processing.The dynamic resource allocation method comprises the steps that S210, user basic information of a user side is obtained; s220, obtaining performance data of the user side according to the user ID; s230, calculating an allocation recommendation score of each user ID according to the performance data; s240, acquiring resource data information; s250, determining user IDs of distributable users of the resource data according to the distribution recommendation score and the resource data information; and S260, the user state and the system CPU utilization rate are monitored in real time based on the distribution result, a CRM system database is updated through a feedback interface, a message flow closed loop is formed, and the resource information distribution timeliness and accuracy can be improved.

Description

[0001] This invention is a divisional application, which is based on the invention patent application filed on December 30, 2022, with application number 202211732551.8 and titled "A business opportunity allocation method, apparatus, computing device and storage medium". Technical Field

[0002] This invention relates to the field of data processing technology, and specifically to a method and apparatus for dynamic resource data allocation based on data processing. Background Technology

[0003] As market competition intensifies, more and more companies are recognizing the importance of CRM (Customer Relationship Management). The efficiency of opportunity allocation often determines the sales efficiency from acquiring valuable customer information to closing deals.

[0004] After customer service or marketing personnel follow up and cleanse customer leads, they will convert leads with potential or interested needs and those meeting service standards into valuable customer information and distribute them to business or sales staff. The appropriate allocation of valuable customer information can improve customer conversion rates and business performance. Currently, business assistants are needed to assist in allocating valuable customer information. This process requires understanding personnel information, then allocating information according to company rules combined with their own subjective judgment. This is time-consuming and labor-intensive, and there are also issues with inaccurate, uneven, or duplicate allocations.

[0005] Therefore, a resource data allocation method is needed that can improve the efficiency and accuracy of resource data allocation in order to solve the problems existing in the prior art. Summary of the Invention

[0006] In view of the above problems, this solution proposes a resource data allocation method based on data processing, including: S210. Obtain basic user information from the user terminal, wherein the basic user information includes at least a user ID, and store the basic user information. S220. Obtain the corresponding performance data based on the user ID. The performance data includes the number of appointments, industry performance data, total performance data, SKU performance data, and SKU order ranking. Associate the performance data with the user ID and store it. S230: Calculate the allocation recommendation score corresponding to each user ID based on the performance data; step S230 includes: Obtain the performance data ranking corresponding to each user ID within a preset time period. The performance data ranking includes appointment ranking, total performance ranking, industry performance ranking, SKU performance ranking, and SKU order ranking. Associate the performance data ranking with the user ID and store it. The weight parameters are dynamically adjusted using a gradient descent-based machine learning model to minimize processor computation error, where the weight parameters are optimized based on real-time feedback data from the message stream. Based on the weighted performance ranking score ratio, calculate the assigned recommendation score for each user ID. S240. Obtain resource data information, wherein the resource data information includes preset operation steps and intention level; S250: Determine the user ID of the user who can allocate the resource data based on the allocation recommendation score and resource data information; step S250 includes: Sort the user IDs according to the allocation recommendation scores to obtain the sorted list of personnel to be assigned, and record the sorted list of personnel to be assigned. The sorting list of personnel to be assigned is filtered according to the preset priority level corresponding to the user ID and the preset operation steps and intention level; Based on the filtered list of personnel to be assigned, determine the user IDs of the personnel who can be assigned to the resource data. Extract a user ID from the user IDs of the personnel who can be assigned and send the obtained resource data information to the extracted user ID. S260. Based on the allocation results, monitor user status and system CPU utilization in real time, update the CRM system database through the feedback interface to form a message flow closed loop. The message flow closed loop includes a data integrity verification module to verify the synchronization between the allocation logic and the hardware interrupt signal. If the allocation fails or the system load exceeds the threshold, a priority queue-based reallocation algorithm is triggered, and computing resources are dynamically allocated in conjunction with the load balancer. The resource data allocation method is executed by a hardware processor.

[0007] With the above solution, when marketers allocate resource data, the program allocates the data in a timely manner based on the allocation recommendation score of the business personnel and the category of the resource data, which can ensure the efficiency and accuracy of resource data allocation.

[0008] Optionally, in the above method, the assigned recommendation score is the total score obtained by multiplying the appointment ranking score, total performance ranking score, industry performance ranking score, SKU performance ranking score, and SKU order volume ranking score within a preset time period by their respective weight parameters.

[0009] Optionally, in the above method, the job type corresponding to the user ID includes department head, junior staff and general business, the preset operation steps include initial contact and offline meeting, and the intention level includes first level, second level and third level, with the first level being higher than the second level and the second level being higher than the third level.

[0010] Optionally, in the above method, step S250, which involves filtering the sorting list of personnel to be assigned based on the preset priority level corresponding to the user ID and the preset operation steps and intention levels, includes: If the preset operation step is the initial contact and the intention level is the first or second level, then filter out the user ID with the highest priority level in the personnel to be assigned sorting list. If the follow-up method is initial contact and the intention level is level three, then filter out user IDs with medium and highest priority levels from the personnel to be assigned list. If the follow-up method is an offline meeting, then filter out the user IDs with the lowest priority level in the personnel to be assigned list; If the filtered list of personnel to be assigned is empty, then obtain the recommended score list of the user ID with the highest priority level, and assign the resource data to the user ID with the highest priority level.

[0011] Optionally, in the above method, the method further includes: The resource data allocation results and resource data tracking status are visualized through the CRM system, and the allocation results are output through real-time API transmission via the CRM system interface.

[0012] According to another aspect of the present invention, a resource data allocation apparatus based on data processing is provided, characterized in that it includes the resource data allocation method based on data processing as described in claim 1, wherein the resource data allocation apparatus comprises: The first acquisition module (510) is used to acquire basic user information, including user ID; The second acquisition module (520) is used to acquire the user's performance data based on the user ID. The performance data includes the number of appointments, industry performance data, total performance data, SKU performance data, and SKU order ranking. The calculation module (530) is used to calculate the allocation recommendation score for each user based on the performance data; The third acquisition module (540) is used to acquire resource data information, which includes preset operation steps and intention levels; The allocation module (550) is used to determine the user ID of the user who can allocate the resource data based on the allocation recommendation score and resource data information.

[0013] According to another aspect of the present invention, a computing device is provided, comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, the program instructions including instructions for performing the resource data allocation method described above.

[0014] According to another aspect of the present invention, a readable storage medium storing program instructions is provided, which, when read and executed by a computing device, causes the computing device to perform the resource data allocation method described above.

[0015] According to the present invention, by acquiring performance data such as total user performance, industry performance, SKU performance, SKU sales volume, and number of appointments, an objective statistical analysis of user business capabilities can be performed. Calculating user allocation recommendation scores based on performance data ranking scores ensures the matching between user profiles and resource data profiles during resource data allocation. Screening resource data allocation personnel based on user allocation recommendation scores combined with resource data follow-up methods and intention levels improves the accuracy of resource data allocation. Message flow processing reduces data overload and improves real-time response speed, and the optimization of the message flow closed loop is verified by log recording to demonstrate the effectiveness of process feedback.

[0016] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A schematic diagram of the structure of a computing device 100 according to an embodiment of the present invention is shown; Figure 2 A flowchart illustrating a resource data allocation method 200 according to an embodiment of the present invention is shown; Figure 3 A schematic diagram illustrating the calculation process of a user's assigned recommendation score according to an embodiment of the present invention is shown; Figure 4 A schematic diagram of a resource data allocation process according to an embodiment of the present invention is shown. Detailed Implementation

[0018] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0019] For businesses, finding leads and profiting from them has always been a goal. Marketing personnel, based on their understanding of industry knowledge and products, use methods such as marketing activities, online information, and telephone inquiries to determine whether customers have the intention to close a deal or have potential needs. After screening, follow-up, and further analysis, leads that match the target customer profile are identified as resource data.

[0020] Once leads are converted into resource data, the efficiency of resource data allocation often determines the efficiency from acquiring resource data to closing an order. Due to a lack of collaboration between the marketing and sales departments, untimely or mismatched resource data handover can easily lead to customer churn.

[0021] To improve the timeliness and accuracy of resource data allocation, this solution provides a resource data allocation method. By combining the performance data of each person in the business department with resource data information, matching business personnel are selected, and resource data is allocated objectively according to the recommendation scores of the business personnel. This improves the accuracy of resource data allocation, reduces uneven or duplicate allocation, and reduces customer churn.

[0022] Figure 1 A schematic diagram of the structure of a computing device 100 according to an embodiment of the present invention is shown. Figure 1 As shown, in the basic configuration 102, the computing device 100 typically includes a system memory 106 and one or more processors 104. A memory bus 108 can be used for communication between the processors 104 and the system memory 106.

[0023] Depending on the desired configuration, processor 104 can be any type of processor, including but not limited to: microprocessors (µP), microcontrollers (µC), digital information processors (DSPs), or any combination thereof. Processor 104 may include one or more levels of cache such as L1 cache 110 and L2 cache 112, processor core 114, and registers 116. Example processor core 114 may include an arithmetic logic unit (ALU), a floating-point unit (FPU), a digital signal processing core (DSP core), or any combination thereof. Example memory controller 118 may be used with processor 104, or in some implementations, memory controller 118 may be an internal part of processor 104.

[0024] Depending on the desired configuration, system memory 106 can be any type of memory, including but not limited to: volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. Physical memory in a computing device typically refers to volatile RAM, and data on a disk needs to be loaded into physical memory before it can be read by processor 104. System memory 106 may include operating system 120, one or more applications 122, and program data 124. In some embodiments, application 122 may be configured to execute instructions on the operating system using program data 124 by one or more processors 104. Operating system 120 may be, for example, Linux, Windows, etc., and includes program instructions for handling basic system services and performing hardware-dependent tasks. Application 122 includes program instructions for implementing various user-desired functions, and application 122 may be, for example, a browser, instant messaging software, software development tools (such as integrated development environments IDEs, compilers, etc.), but is not limited to these. When application 122 is installed in computing device 100, driver modules may be added to operating system 120.

[0025] When computing device 100 starts up, processor 104 reads and executes program instructions from memory 106 of operating system 120. Application 122 runs on operating system 120, utilizing interfaces provided by operating system 120 and underlying hardware to implement various user-expected functions. When user starts application 122, application 122 is loaded into memory 106, and processor 104 reads and executes program instructions from memory 106 of application 122.

[0026] The computing device 100 also includes a storage device 132, which includes a removable storage device 136 and a non-removable storage device 138, both of which are connected to a storage interface bus 134.

[0027] The computing device 100 may also include an interface bus 140 that facilitates communication from various interface devices (e.g., output devices 142, peripheral interfaces 144, and communication devices 146) to the basic configuration 102 via a bus / interface controller 130. Example output devices 142 include a graphics processing unit 148 and an audio processing unit 150. They may be configured to facilitate communication with various external devices such as displays or speakers via one or more A / V ports 152. Example peripheral interfaces 144 may include a serial interface controller 154 and a parallel interface controller 156, which may be configured to facilitate communication with external devices such as input devices (e.g., keyboards, mice, pens, voice input devices, touch input devices) or other peripherals (e.g., printers, scanners, etc.) via one or more I / O ports 158. Example communication devices 146 may include a network controller 160, which may be arranged to facilitate communication with one or more other computing devices 162 via a network communication link through one or more communication ports 164.

[0028] A network communication link can be an example of a communication medium. A communication medium can typically be embodied in computer-readable instructions, data structures, or program modules within a modulated data signal, such as a carrier wave or other transmission mechanism, and can include any information delivery medium. A “modulated data signal” can be a signal whose data set, or whose modifications, can be encoded with information within the signal. As a non-limiting example, a communication medium can include wired media such as wired networks or leased lines, and various wireless media including sound, radio frequency (RF), microwave, infrared (IR), or other wireless media. The term “computer-readable medium” as used herein can include both storage media and communication media. In the computing device 100 according to the invention, application 122 includes instructions for performing the resource data allocation method 200 of the invention.

[0029] Figure 2 A flowchart illustrating a resource data allocation method 200 according to an embodiment of the present invention is shown. Figure 2 As shown, the method begins with step S210, which obtains basic user information, including at least the user ID.

[0030] In this embodiment of the invention, "user" refers to business or sales personnel, primarily responsible for business negotiations, processing orders or contracts, and handling customer follow-up and management. Basic user information may include the user's company, department, and job type. Because user IDs are unique, performance data can be retrieved using a big data platform based on the user ID.

[0031] Since job type or level can reflect a person's work ability and experience to a certain extent, obtaining a user's job type allows for more accurate allocation of resource data. Job types can be defined according to specific job levels, for example, they can be divided into department head, team leader (basic staff), and general business personnel.

[0032] Then, step S220 is executed to obtain the user's performance data based on the user ID. The performance data includes the number of appointments, industry performance data, total performance data, SKU performance data, and SKU order ranking.

[0033] The performance data of business personnel within a preset time period can reflect their sales or negotiation abilities to a certain extent. For example, it can provide information on the success rate of closing deals over the past six months, sales performance and volume of different product categories (consulting products, project products, etc.), SKU (service product code) performance and number of deals closed.

[0034] Then, step S230 is executed to calculate the allocation recommendation score for each user ID based on the performance data.

[0035] Figure 3 A schematic diagram illustrating the calculation process of assigning recommendation scores to user IDs according to an embodiment of the present invention is shown. Figure 3 As shown, based on the user ID, you can query each user's total performance data, industry performance data, and SKU performance data. Then, based on the total number of users, obtain each user's appointment ranking, total performance ranking, industry performance ranking, SKU performance ranking, and SKU order ranking. Next, based on the number of users to be assigned and each user's performance data ranking within a preset time period, calculate each user's performance ranking score. The performance ranking score equals the number of users to be assigned minus the performance ranking plus 1. Finally, based on the proportion of the performance ranking score to the number of users to be assigned, calculate each user's allocation recommendation score. The allocation recommendation score is the sum of the appointment ranking score, total performance ranking score, industry performance ranking score, SKU performance ranking score, and SKU order ranking score within the preset time period, multiplied by their respective weight parameters.

[0036] For example, let N be the total number of business personnel involved in the allocation. If a user's performance ranking for consulting products within the past six months is X1, then the performance ranking score Y1 = N - X1 + 1; if the user's number of signed contracts for consulting products within the past six months ranks X2, then the number of signed contracts ranking score Y2 = N - X2 + 1; and if the user's number of appointments in the past three months ranks X3, then the number of appointments ranking score Y3 = N - X3 + 1. Therefore, we can calculate each user's performance score, number of signed contracts score, and number of appointments score for different product categories within a preset time period, and then add them together to obtain the total allocation recommendation score.

[0037] In one embodiment of the present invention, the product's performance score for the past six months is G1 = K1 * (Y1 / N) * 1000; the product's order volume score for the past six months is G2 = K2 * (Y2 / N) * 1000; and the total number of appointments in the past three months is G3 = K3 * (Y3 / N) * 1000. The total allocation recommendation score is then G = G1 + G2 + G3, where K1, K2, and K3 are pre-set weighting parameters, and K1 + K2 + K3 = 1. Y1 is the product's performance ranking score within the past six months, Y2 is the product's order volume score within the past six months, Y3 is the product's appointment frequency score within the past three months, and N is the total number of people to be allocated. It should be noted that the statistical time for each performance data point can be the same or different; this solution does not impose any limitations on this.

[0038] Next, step S240 is executed to obtain resource data information, which includes preset operation steps and intention levels.

[0039] In one embodiment of the present invention, the preset operation steps may include initial contact and offline meeting, and the level of intent may be set as a first level, a second level, and a third level, with the first level being higher than the second level and the second level being higher than the third level.

[0040] Finally, step S250 is executed to determine the allocatable users of the resource data based on the allocation recommendation score and resource data information.

[0041] Specifically, first, users are sorted from highest to lowest based on their assigned recommendation scores, resulting in a ranked list of personnel to be assigned. Then, this ranked list is filtered based on the user's job type, preset operation steps, and level of interest. Finally, the allocable personnel for the resource data are determined based on the filtered ranked list.

[0042] When allocating resource data, you can select branch offices in different regions and allocate resource data in a targeted manner based on the products consulted by customers and the company and department to which the user belongs.

[0043] Generally, the chances of closing a deal with a client in one go are low; most collaborations are achieved through follow-up. Follow-up methods include initial contact and in-person meetings. Initial contact can be via phone or email, while in-person meetings are for follow-ups after the initial meeting where further cooperation is planned. Clients' intentions can be categorized into different levels based on their actual situation, for example, Level A, Level B, and Level C, with Level A being higher than Level B, and Level B being higher than Level C. The list of personnel to be assigned can be filtered according to preset rules.

[0044] Figure 4 A schematic diagram of a resource data allocation process according to an embodiment of the present invention is shown. Figure 4As shown, if the preset operation step is initial contact and the intention level is Level 1 A or Level 2 B, then the department heads in the personnel to be assigned list are filtered out, that is, non-department heads are retained; if the follow-up method is initial contact and the intention level is Level 3 C, then the basic staff and department heads in the personnel to be assigned list are filtered out, and ordinary business personnel are retained; if the follow-up method is offline meeting, then the ordinary business personnel in the personnel to be assigned list are filtered out, and basic staff and department heads are retained.

[0045] In other words, resource data with low cooperation intentions are allocated to ordinary business personnel, while resource data with high cooperation intentions are allocated to junior staff and department heads.

[0046] To prevent duplicate allocation, we can first check if the resource data has already been allocated to business personnel. If not, we can continue with personnel allocation. To prevent uneven allocation, we can check if the number of resource data allocation attempts exceeds a preset limit. If the number of resource data allocation attempts does not exceed the preset limit, we can continue with resource data allocation.

[0047] For example, for personnel P1 (department head), P2 (junior staff), and P3 (general business), P1 scores 100, P2 scores 90, and P3 scores 80. The follow-up method is initial contact, and the intention level is A. Then the order of returning personnel is: P2, P3.

[0048] If the filtered list of personnel to be assigned is empty, then obtain the list of department heads whose assignment recommendation scores are ranked. For example, directly obtain the top 5 department heads by their assignment recommendation scores for backup assignment.

[0049] In one embodiment of the present invention, resource data can be visually allocated through a CRM system. During the actual allocation process, marketing personnel can update the resource data status in real time based on customer follow-up, and sales personnel can view resource data allocation, follow-up, and conversion rates within the CRM system. Furthermore, after resource data allocation, the CRM system can notify sales personnel via mobile devices, avoiding delays in follow-up. When marketing personnel receive the personnel ranking list on the client side, they can select sales personnel for resource data allocation based on the allocation recommendation score details.

[0050] The resource data allocation device 500 includes a first acquisition module 510, a second acquisition module 520, a calculation module 530, a third acquisition module 540, and an allocation module 550.

[0051] The first acquisition module 510 can acquire basic user information, including user ID; the second acquisition module 520 can acquire user performance data based on user ID, including appointment count, industry performance data, total performance data, SKU performance data, and SKU order ranking; the calculation module 530 can calculate the allocation recommendation score for each user based on the performance data; the third acquisition module 540 can acquire resource data information, including preset operation steps and intention levels; and the allocation module 550 can determine the allocatable users of the resource data based on the allocation recommendation score and the resource data information.

[0052] By acquiring users' performance data within a specified timeframe, their business capabilities can be assessed, which is beneficial for subsequent resource allocation.

[0053] To allocate resource data to users more intuitively and objectively, performance data can be quantified to obtain user business capability profiles, ensuring the matching between user profiles and resource data profiles during subsequent resource data allocation.

[0054] By filtering the list of personnel to be allocated based on the allocation recommendation score, the follow-up method of resource data, and the level of intention, the accuracy of resource data allocation can be further improved.

[0055] The above solution allows for the statistical analysis of user business capabilities by acquiring user performance data. Calculating user allocation recommendation scores based on performance data ensures the matching between user profiles and resource data profiles during resource data allocation. Allocating customer resource data based on user allocation recommendation scores improves the efficiency and accuracy of resource data allocation. Furthermore, real-time tracking and feedback of resource data progress enhances marketing collaboration efficiency.

[0056] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0057] Similarly, it should be understood that, in order to streamline this disclosure and aid in understanding one or more of the various aspects of the invention, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, this method of disclosure should not be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into this detailed description, wherein each claim itself is a separate embodiment of the invention.

[0058] Those skilled in the art will understand that modules, units, or components of the devices disclosed in the examples herein can be arranged in the devices described in this embodiment, or alternatively, can be located in one or more devices different from the devices in this example. The modules in the foregoing examples can be combined into a single module or, in addition, can be divided into multiple sub-modules.

[0059] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0060] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0061] Furthermore, some of the embodiments are described herein as methods or combinations of method elements that can be implemented by a processor of a computer system or by other means of performing functions. Therefore, a processor having the necessary instructions for implementing a method or method element forms means for implementing that method or method element. Furthermore, the elements described herein in the apparatus embodiments are examples of means for implementing functions performed by elements for the purposes of carrying out the invention.

[0062] As used herein, unless otherwise specified, the use of ordinal numbers such as “first,” “second,” “third,” etc., to describe ordinary objects merely indicates different instances of similar objects and is not intended to imply that the objects being described must have a given order in time, space, ordering, or any other manner.

[0063] Although the invention has been described with respect to a limited number of embodiments, those skilled in the art will understand from the foregoing description that other embodiments are conceivable within the scope of the invention described herein. Furthermore, it should be noted that the language used in this specification has been chosen primarily for readability and edibility purposes, and not for the purpose of interpreting or limiting the subject matter of the invention. Therefore, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the invention is illustrative rather than restrictive, and the scope of the invention is defined by the appended claims.

Claims

1. A dynamic resource allocation method based on data processing, characterized in that, include: S210. Obtain basic user information from the user terminal, wherein the basic user information includes at least a user ID, and store the basic user information. S220. Obtain the corresponding performance data based on the user ID. The performance data includes the number of appointments, industry performance data, total performance data, SKU performance data, and SKU order ranking. Associate the performance data with the user ID and store it. S230. Calculate the allocation recommendation score corresponding to each user ID based on the performance data; Step S230 includes: Obtain the performance data ranking corresponding to each user ID within a preset time period. The performance data ranking includes appointment ranking, total performance ranking, industry performance ranking, SKU performance ranking, and SKU order ranking. Associate the performance data ranking with the user ID and store it. The weight parameters are dynamically adjusted using a gradient descent-based machine learning model to minimize processor computation error, where the weight parameters are optimized based on real-time feedback data from the message stream. Based on the weighted performance ranking score ratio, calculate the assigned recommendation score for each user ID. S240. Obtain resource data information, wherein the resource data information includes preset operation steps and intention level; S250: Determine the user ID of the user who can allocate the resource data based on the allocation recommendation score and resource data information; step S250 includes: Sort the user IDs according to the allocation recommendation scores to obtain the sorted list of personnel to be assigned, and record the sorted list of personnel to be assigned. The sorting list of personnel to be assigned is filtered according to the preset priority level corresponding to the user ID and the preset operation steps and intention level; Based on the filtered list of personnel to be assigned, determine the user IDs of the personnel who can be assigned to the resource data. Extract a user ID from the user IDs of the personnel who can be assigned and send the obtained resource data information to the extracted user ID. S260. Based on the allocation results, monitor user status and system CPU utilization in real time, update the CRM system database through the feedback interface to form a message flow closed loop. The message flow closed loop includes a data integrity verification module to verify the synchronization between the allocation logic and the hardware interrupt signal. If the allocation fails or the system load exceeds the threshold, a priority queue-based reallocation algorithm is triggered, and computing resources are dynamically allocated in conjunction with the load balancer. The resource data allocation method is executed by a hardware processor.

2. The dynamic resource allocation method based on data processing according to claim 1, characterized in that, The assigned recommendation score is the total score obtained by multiplying the appointment ranking score, total performance ranking score, industry performance ranking score, SKU performance ranking score, and SKU order volume ranking score within a preset time period by their respective weight parameters.

3. The dynamic resource allocation method based on data processing according to claim 1, characterized in that, The job types corresponding to the user IDs include department heads, junior staff, and general business personnel. The preset operation steps include initial contact and offline meetings. The intention levels include first level, second level, and third level, with the first level being higher than the second level and the second level being higher than the third level.

4. The dynamic resource allocation method based on data processing according to claim 3, characterized in that, Step S250, which involves filtering the sorting list of personnel to be assigned based on the preset priority level corresponding to the user ID and the preset operation steps and intention levels, includes: If the preset operation step is the initial contact and the intention level is the first or second level, then filter out the user ID with the highest priority level in the personnel to be assigned sorting list. If the follow-up method is initial contact and the intention level is level three, then filter out user IDs with medium and highest priority levels from the personnel to be assigned list. If the follow-up method is an offline meeting, then filter out the user IDs with the lowest priority level in the personnel to be assigned list; If the filtered list of personnel to be assigned is empty, then obtain the recommended score list of the user ID with the highest priority level, and assign the resource data to the user ID with the highest priority level.

5. The dynamic resource allocation method based on data processing according to claim 1, characterized in that, The method further includes: The resource data allocation results and resource data tracking status are visualized through the CRM system, and the allocation results are output through real-time API transmission via the CRM system interface.

6. A dynamic resource allocation device based on data processing, characterized in that, For implementing the data processing-based resource data allocation method as described in claim 1, the resource data allocation device includes: The first acquisition module (510) is used to acquire basic user information, including user ID; The second acquisition module (520) is used to acquire the user's performance data based on the user ID. The performance data includes the number of appointments, industry performance data, total performance data, SKU performance data, and SKU order ranking. The calculation module (530) is used to calculate the allocation recommendation score for each user based on the performance data; The third acquisition module (540) is used to acquire resource data information, which includes preset operation steps and intention levels; The allocation module (550) is used to determine the user ID of the user who can allocate the resource data based on the allocation recommendation score and resource data information.

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