Dynamic intelligent order distribution method and device based on multi-dimensional weight, equipment, medium and product

Abstract

The invention discloses a dynamic intelligent order distribution method, device and equipment based on multi-dimensional weight, a medium and a product, and relates to the technical field of e-commerce customer services. The method comprises the following steps of: after receiving a service request list, respectively acquiring a customer identity influence coefficient corresponding to a customer identity identifier, a service basic weight corresponding to a service type identifier and an aging basic weight corresponding to service request time by querying multi-source data; the method comprises the following steps: acquiring a business request list, calculating by applying a multi-dimensional comprehensive weight calculation engine to obtain a multi-dimensional comprehensive weight, inserting the business request list into a business request list distribution queue based on the weight, and dynamically reordering the list distribution queue by applying a calculation engine based on a current customer identity influence coefficient, an aging basic weight and / or a handling urgency influence coefficient, and finally, any service request list at the first position of the queue is allocated to the target service agent terminal, so that the list allocation accuracy and efficiency can be greatly improved, and the service quality and the customer experience can be improved.

Description

Technical Field

[0001] This invention belongs to the field of e-commerce customer service technology, specifically relating to a dynamic intelligent order allocation method, device, equipment, medium and product based on multi-dimensional weights, which can be applied to scenarios such as online customer service and work order processing. Background Technology

[0002] In modern e-commerce operations, efficiently and accurately assigning customer-initiated business requests (such as order inquiries, after-sales applications, and complaints / suggestions) to appropriate customer service agents is key to improving customer satisfaction and operational efficiency. Traditional order allocation systems typically employ order allocation strategies based on fixed rules, such as setting static priorities according to business type, or using simple round-robin / load balancing algorithms.

[0003] As business complexity increases and customers' demands for service timeliness continue to rise, these traditional order splitting methods are gradually revealing many limitations: (1) The current order allocation strategy is too simplistic and cannot adapt to complex and ever-changing business scenarios. Existing order allocation systems based on business type priority assign a fixed priority value to each business type and then simply sort and allocate orders according to this value. However, this static order allocation method has significant drawbacks: First, it fails to reflect the differences and importance of different customers (such as VIP customers and regular customers) within the same business type; second, it ignores the real-time urgency of the business. For example, a flight rescheduling request close to departure time and a flight inquiry request for next week might be treated equally in a traditional order allocation system, which clearly does not meet actual operational needs. This single-dimensional order allocation strategy leads to unreasonable resource allocation, preventing high-value or high-urgency business requests from being prioritized and reducing overall service efficiency.

[0004] (2) Rigid weight management and insufficient system flexibility and scalability. Another type of existing technology attempts to introduce the concept of weight, but its weight management method is relatively crude and rigid. When adding or modifying a business type, developers often need to modify the underlying code and redeploy, resulting in long development cycles and high costs, slow business response, and difficulty in supporting the rapidly iterating Internet business needs. In addition, the existing weight calculation models are mostly linear superpositions, lacking a mechanism for dynamic collaborative calculation of multi-dimensional factors (such as customer identity, business type, and real-time timeliness), and cannot accurately quantify the multi-dimensional comprehensive weight that can reflect the true priority of business requests. The accuracy of order allocation needs to be improved.

[0005] (3) Poor dynamic adaptability, unable to cope with real-time changing service demands. In a real-time operating environment, the urgency of a business changes dynamically over time, and most existing order allocation systems lack an effective dynamic reordering mechanism. Once a business request enters the order allocation queue, its position is basically fixed unless a request with a higher static priority is inserted. This results in some requests that, although initially low in priority, become increasingly urgent over time (e.g., flight rescheduling requests close to departure time) not being processed in a timely manner. Existing technologies fail to effectively combine "time decay" or the urgency impact coefficient negatively correlated with the business deadline with real-time reordering of the queue, thus failing to achieve truly intelligent dynamic order allocation.

[0006] (4) Weak access control mechanisms pose operational security risks. Specifically, existing order allocation systems typically lack fine-grained access control deeply integrated with the organizational structure. This can lead to unauthorized operations; for example, a department's business administrator might modify the global order allocation strategy, disrupting overall operations. Furthermore, the lack of a robust operation record traceability mechanism makes it difficult to pinpoint problems and assign responsibility when order allocation disputes or errors occur, reducing system reliability and auditability.

[0007] In summary, there is an urgent need in this field for an intelligent order allocation method that can effectively address any one or any combination of the aforementioned shortcomings, so as to significantly improve the accuracy, efficiency, and system security of order allocation through multi-dimensional dynamic weight calculation capabilities, high configurability and flexibility, adaptability to real-time business changes, and / or strict security access control. Summary of the Invention

[0008] The purpose of this invention is to provide a dynamic intelligent order allocation method, device, computer equipment, computer-readable storage medium, and computer program product based on multi-dimensional weights, in order to solve the problems of existing order allocation technologies, such as single order allocation strategy, rigid weight management, poor dynamic adaptability, and / or weak permission and control mechanisms.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, a dynamic intelligent order allocation method based on multi-dimensional weights is provided, including: Receive business request forms carrying customer identification, business type identification, and business deadline; Based on the customer identity identifier, the business type identifier, and the business request time of the business request form, by querying multi-source data, the customer identity influence coefficient corresponding to the customer identity identifier, the basic business weight corresponding to the business type identifier, and the basic timeliness weight corresponding to the business request time are obtained respectively. Based on the customer identity impact coefficient, the basic business weight, the basic timeliness weight, and the urgency impact coefficient determined by the absolute value of the time difference between the business request time and the business deadline and negatively correlated with that absolute value, a multi-dimensional comprehensive weight calculation engine is applied to calculate the multi-dimensional comprehensive weight of the business request form. The multi-dimensional comprehensive weight calculation engine calculates the weight according to the following formula:

[0010] In the formula, This represents the result of the multidimensional comprehensive weight calculation. Indicates the basic weight of the business. This indicates the influence coefficient of customer identity. Indicates the timeliness-based weight. Indicates the urgency factor of the process; Based on the multi-dimensional comprehensive weight of the business request form, the business request form is inserted into the business request allocation queue, wherein the business request allocation queue includes at least one business request form to be allocated and arranged in descending order of weight. Based on the current customer identity impact coefficient, the current timeliness base weight, and / or the current processing urgency impact coefficient, the multi-dimensional comprehensive weight calculation engine is used to dynamically reorder the business request order queue. The current timeliness base weight corresponds to the current time, and the current processing urgency impact coefficient is negatively correlated with the current absolute value of the time difference. The current absolute value of the time difference refers to the absolute value of the time difference between the current time and the business deadline. Any business request that is at the top of the business request queue after reordering will be assigned to the target service agent terminal.

[0011] Based on the above-mentioned invention, a novel intelligent order allocation scheme is provided, which dynamically calculates multi-dimensional weights and dynamically reorders the order allocation queue based on customer identity, business type, and real-time timeliness. Specifically, upon receiving a business request carrying a customer identity identifier, business type identifier, and business deadline, the scheme first queries multi-source data to obtain the customer identity impact coefficient corresponding to the customer identity identifier, the basic business weight corresponding to the business type identifier, and the basic timeliness weight corresponding to the business request time. A multi-dimensional comprehensive weight calculation engine is then used to calculate the multi-dimensional comprehensive weight. Based on this weight, the business request is inserted into the order allocation queue. Then, based on the current customer identity impact coefficient, the basic timeliness weight, and / or the urgency impact coefficient, the calculation engine dynamically reorders the order allocation queue. Finally, any business request at the top of the queue is assigned to the target service agent terminal. This scheme provides multi-dimensional dynamic weight calculation capabilities and adaptability to real-time business changes, significantly improving the accuracy and efficiency of order allocation, thereby enhancing service quality and customer experience, and facilitating practical application and promotion.

[0012] In one possible design, based on the customer identity identifier, the customer identity influence coefficient corresponding to the customer identity identifier is obtained by querying multi-source data, including: Based on the customer identity identifier, real-time operational data and historical service records corresponding to the customer identity identifier are obtained by querying multi-source data; Extract the current customer level corresponding to the customer identity identifier from the real-time operation data, and perform statistical analysis on the historical service records to obtain the number of historical complaints and / or the frequency of historical complaints corresponding to the customer identity identifier; Based on the customer's current level and the number and / or frequency of historical complaints, a customer identity influence coefficient corresponding to the customer's identity identifier is determined, wherein the customer identity influence coefficient is positively correlated with the customer's current level and also positively correlated with the number and / or frequency of historical complaints.

[0013] In one possible design, the urgency factor is determined as follows: When the absolute value of the time difference is less than or equal to the first time threshold, the urgency impact coefficient is determined. ; When the absolute value of the time difference is greater than the first time threshold and less than or equal to the second time threshold, the urgency impact coefficient is determined. Wherein, the second time threshold is greater than the first time threshold. and Represent pure decimals and have , This represents the absolute value of the time difference; When the absolute value of the time difference is greater than the second time threshold, the urgency impact coefficient is determined. .

[0014] In one possible design, based on the current customer identity impact coefficient, the current timeliness base weight, and / or the current processing urgency impact coefficient, the multi-dimensional comprehensive weight calculation engine is applied to dynamically reorder the business request order queue, including: When a business request is assigned every unit of time or when the first business request in the business request allocation queue is assigned, the following steps are performed: The following steps are taken to iterate through the consecutive business request queues to be assigned, selected sequentially from front to back using a sliding window: Based on the current multidimensional comprehensive weight of the currently iterated consecutive business request queues, it is determined whether the difference in the current multidimensional comprehensive weight of each pair of adjacent business request queues is less than or equal to a preset weight threshold. If so, the current multidimensional comprehensive weight of the currently iterated consecutive business request queues is updated using the multidimensional comprehensive weight calculation engine based on the current customer identity impact coefficient, the current timeliness base weight, and / or the current processing urgency impact coefficient. Based on the update result, the currently iterated consecutive business request queues are rearranged in descending order of weight to dynamically reorder the business request allocation queue. Then, the next group of consecutive business request queues is iterated through. Otherwise, the next group of consecutive business request queues is directly iterated through. The width of the sliding window is... A business request form, The sliding window represents a positive integer greater than or equal to 2, and its sliding step size is... A business request form, Indicates less than or equal to The positive integer, the current timeliness base weight corresponds to the current time, the current processing urgency impact coefficient is negatively correlated with the current time difference absolute value, and the current time difference absolute value refers to the absolute value of the time difference between the current time and the business deadline.

[0015] In one possible design, the method further includes, before querying multi-source data: Through a visual human-computer interaction interface, the administrator can obtain the configuration results of the mapping relationship between business type identifier and business basic weight, the mapping relationship between customer identity identifier and customer identity influence coefficient, the determination rules of customer identity influence coefficient, the mapping relationship between time and timeliness basic weight, and / or the determination rules of urgency influence coefficient. Based on the two-way data binding technology of the front-end framework, a multi-dimensional comprehensive weight calculation engine is applied to calculate the multi-dimensional comprehensive weight under different configuration results, and the calculation results are previewed and displayed on the human-computer interaction interface. The multi-dimensional comprehensive weight calculation engine calculates the weight according to the following formula:

[0016] In the formula, This represents the result of the multidimensional comprehensive weight calculation. Indicates the basic weight of the business. This indicates the influence coefficient of customer identity. Indicates the timeliness-based weight. Indicates the urgency factor of the process; The confirmed configuration results are stored using a decoupled JSON structure for access and query.

[0017] In one possible design, the target service agent terminal is determined based on the order allocation strategy. The configuration permissions and / or viewing permissions of the order allocation strategy are mapped to the organizational structure tree through the RBAC model. Before the configuration operation and / or viewing operation of the order allocation strategy, the mapping result is applied to verify whether the user is within the scope of the user's organizational structure. If so, the operation is permitted and the corresponding operation log is recorded; otherwise, the operation is prohibited. The operation log contains the user's unique identifier, the operation timestamp, and the comparison results of the strategy parameters before and after the operation.

[0018] Secondly, a dynamic intelligent order allocation device based on multi-dimensional weights is provided, including a business request receiving unit, a multi-source data query unit, a comprehensive weight calculation unit, a business request queue insertion unit, a queue dynamic rearrangement unit, and a business request allocation unit that are connected in sequence. The business request receiving unit is used to receive a business request form carrying a customer identity identifier, a business type identifier, and a business deadline. The multi-source data query unit is used to obtain, based on the customer identity identifier, the business type identifier, and the business request time of the business request form, the customer identity influence coefficient corresponding to the customer identity identifier, the business basic weight corresponding to the business type identifier, and the timeliness basic weight corresponding to the business request time by querying multi-source data. The comprehensive weight calculation unit is used to calculate the multi-dimensional comprehensive weight of the business request form based on the customer identity impact coefficient, the business basic weight, the timeliness basic weight, and the processing urgency impact coefficient determined based on the absolute value of the time difference between the business request time and the business deadline and negatively correlated with the absolute value of the time difference. The multi-dimensional comprehensive weight calculation engine calculates the weight according to the following formula:

[0019] In the formula, This represents the result of the multidimensional comprehensive weight calculation. Indicates the basic weight of the business. This indicates the influence coefficient of customer identity. Indicates the timeliness-based weight. Indicates the urgency factor of the process; The business request queueing unit is used to insert the business request order into the business request allocation queue based on the multi-dimensional comprehensive weight of the business request order, wherein the business request allocation queue includes at least one business request order to be allocated and arranged in descending order of weight. The queue dynamic reordering unit is used to dynamically reorder the business request order queue based on the current customer identity impact coefficient, the current timeliness base weight, and / or the current processing urgency impact coefficient, using the multi-dimensional comprehensive weight calculation engine. The current timeliness base weight corresponds to the current time, and the current processing urgency impact coefficient is negatively correlated with the current absolute value of the time difference. The current absolute value of the time difference refers to the absolute value of the time difference between the current time and the business deadline. The service request allocation unit is used to allocate any service request that is at the top of the service request queue after reordering to the target service agent terminal.

[0020] Thirdly, the present invention provides a computer device comprising a storage module, a processing module, and a transceiver module connected in sequence for communication, wherein the storage module is used to store a computer program, the transceiver module is used to send and receive messages, and the processing module is used to read the computer program and execute the dynamic intelligent order allocation method as described in the first aspect or any possible design in the first aspect.

[0021] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, perform the dynamic intelligent order splitting method as described in the first aspect or any possible design within the first aspect.

[0022] Fifthly, the present invention provides a computer program product, including a computer program or instructions, wherein the computer program or instructions, when executed by a computer, implement the dynamic intelligent order splitting method as described in the first aspect or any possible design in the first aspect.

[0023] The beneficial effects of the above scheme are: (1) This invention creatively provides a new intelligent order allocation scheme that dynamically calculates multi-dimensional weights and dynamically rearranges the order allocation queue based on customer identity, business type and real-time timeliness. After receiving a business request carrying customer identity identifier, business type identifier and business deadline, the system first obtains the customer identity influence coefficient corresponding to the customer identity identifier, the business basic weight corresponding to the business type identifier and the timeliness basic weight corresponding to the business request time by querying multi-source data. The system then calculates the multi-dimensional comprehensive weight using a multi-dimensional comprehensive weight calculation engine. Based on this weight, the business request is inserted into the business request allocation queue. Based on the current customer identity influence coefficient, timeliness basic weight and / or urgency influence coefficient, the system dynamically rearranges the order allocation queue using a calculation engine. Finally, any business request at the top of the queue is allocated to the target service agent terminal. This provides a multi-dimensional dynamic weight calculation capability and an adaptive capability to real-time business changes, which greatly improves the accuracy and efficiency of order allocation, thereby improving the quality of business services and customer experience. (2) It can significantly improve the accuracy and intelligence of order allocation. For the first time, the basic weight of business, the customer identity influence coefficient, the basic weight of timeliness and the urgency influence coefficient that is negatively correlated with the deadline are calculated together to generate a multi-dimensional comprehensive weight that can fully and accurately reflect the real-time value and urgency of business requests. This can effectively distinguish the needs of different VIP level customers under the same business type and identify businesses that are close to the deadline and have high urgency (such as flight ticket rescheduling close to departure). This enables differentiated and accurate dynamic intelligent order allocation, ensuring that high-value and high-urgency requests are given priority and the order allocation accuracy is greatly improved. (3) It can fundamentally enhance the dynamic adaptability and real-time response capability of the system. That is, it not only sorts new requests when they are entered into the database, but also introduces a dynamic reordering mechanism based on a sliding window. This mechanism can periodically or triggerically recalculate the weight of requests in the queue based on the latest parameters at the current time (such as the updated processing urgency impact coefficient). This can successfully overcome the drawbacks of rigid queues in traditional systems, and enable some requests whose urgency increases sharply over time (such as after-sales orders that are about to be overdue for compensation) to be automatically moved forward. This realizes the "real-time perception and dynamic adjustment" of the order queue, greatly ensuring the processing timeliness of key businesses and avoiding operational losses caused by system delay response. (4) It can greatly optimize the flexibility of strategy configuration and the efficiency of development and maintenance. That is, through the visual configuration interface, real-time preview of two-way data binding and decoupled JSON storage structure, the "what you see is what you get" configuration of the order allocation strategy is realized. In this way, without the intervention of developers, the weight mapping relationship or calculation rules can be quickly adjusted, and the configuration cycle of adding or modifying business types is shortened from the "person-day" level to the "hour" level, which greatly reduces the development cost and maintenance complexity of the system and strongly supports the rapid iteration of business. (5) It can comprehensively consolidate the precision of access control and the security of system operation. That is, by deeply mapping the RBAC model with the organizational structure tree, fine-grained access control for order allocation strategy configuration and viewing is realized. This can effectively eliminate the risk of cross-departmental and unauthorized operations, ensure the standardization and security of order allocation strategy management, and can be combined with detailed operation log recording and parameter comparison function to realize the traceability and auditability of the entire operation chain, providing a solid data foundation for problem location and responsibility definition. The overall reliability and compliance of the system are qualitatively improved. (6) It can effectively guarantee the robustness of the algorithm and the fairness of the sorting. That is, by using the piecewise function determination mechanism of the urgency impact coefficient and the conflict detection and secondary sorting mechanism based on the sliding window and the weight difference threshold, the robustness of the system is enhanced (because the piecewise function model makes the urgency calculation more in line with the actual business logic; while the conflict detection mechanism avoids the frequent sorting of requests with very close weights due to small calculation fluctuations, and only initiates re-sorting when necessary). It not only guarantees the fairness and rationality of the sorting, but also avoids unnecessary computational overhead, maintains the stability of system performance, and is convenient for practical application and promotion. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart illustrating the dynamic intelligent order allocation method based on multidimensional weights provided in the embodiments of this application.

[0026] Figure 2 This is a schematic diagram of the structure of the dynamic intelligent order allocation device based on multidimensional weights provided in the embodiments of this application.

[0027] Figure 3 A schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these embodiments without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0029] It should be understood that although the terms "first" and "second", etc., may be used herein to describe various objects, these objects should not be limited by these terms. These terms are only used to distinguish one object from another. For example, the first object may be referred to as the second object, and similarly, the second object may be referred to as the first object, without departing from the scope of the exemplary embodiments of the invention.

[0030] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, or A and B exist simultaneously. Another example is A, B and / or C, which can mean that any one of A, B, and C or any combination thereof exists. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone or A and B exist simultaneously. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0031] Example like Figure 1 As shown, the dynamic intelligent order allocation method based on multi-dimensional weights provided in the first aspect of this embodiment can be executed, but is not limited to, by computer devices with certain computing resources, such as servers, personal computers (PCs, referring to a type of multi-purpose computer suitable for personal use in terms of size, price, and performance; desktops, laptops, mini-laptops, tablets, and ultrabooks are all considered personal computers), smartphones, personal digital assistants (PDAs), or wearable devices. Figure 1 As shown, the dynamic intelligent order allocation method includes, but is not limited to, the following steps S1 to S6.

[0032] S1. Receive a business request form that carries, but is not limited to, customer identification, business type identification, and business deadline.

[0033] In step S1, the service request form is a service order (e.g., a flight booking request, flight rescheduling request, flight cancellation request, flight inquiry request, or complaint / suggestion request) to be assigned to a service agent terminal. It can be routinely initiated by customers via electronic devices such as smartphones or tablets. The customer identification identifier is used to uniquely identify the customer, specifically, but not limited to, an ID card number, mobile phone number, or customer account. The service type identifier is used to uniquely identify the service type to which the request belongs, specifically, but not limited to, a service change type, a service inquiry type, or a service complaint type. The service deadline reflects the processing time limit for the service request form, and is particularly applicable to service request forms with time limits, such as flight booking requests, flight rescheduling requests, and flight cancellation requests (if there are service request forms without time limits, the corresponding service deadline can be set to infinity).

[0034] S2. Based on the customer identity identifier, the business type identifier, and the business request time of the business request form, by querying multi-source data, obtain the customer identity influence coefficient corresponding to the customer identity identifier, the basic business weight corresponding to the business type identifier, and the basic timeliness weight corresponding to the business request time.

[0035] In step S2, the multi-source data includes, but is not limited to, mapping relationship data between business type identifiers and basic business weights (e.g., the basic business weight for business change type is 80; the basic business weight for business consultation type is 50; the basic business weight for business complaint type is 100; etc.), mapping relationship data between customer identity identifiers and customer identity influence coefficients (e.g., when the customer identity identifier indicates a platinum member level, the customer identity influence coefficient is 50%; when the customer identity identifier indicates a gold member level, the customer identity influence coefficient is 30%; when the customer identity identifier indicates a regular member level, the customer identity influence coefficient is 0%; etc.), and data on the rules for determining the customer identity influence coefficient (e.g., the positive correlation function between the customer identity influence coefficient and the customer level, i.e., the aforementioned customer identity identifier). This includes data on the mapping relationship between customer identity impact coefficient and customer identity impact coefficient; a positive correlation function between customer identity impact coefficient and historical complaint count and / or historical complaint frequency; etc.), data on the mapping relationship between time and timeliness base weight (e.g., timeliness base weight of 20 from 0:00 to 6:00; timeliness base weight of 10 from 6:00 to 24:00; etc.), data on the determination rules for the urgency impact coefficient (e.g., a negative correlation function between the urgency impact coefficient and the absolute value of the time difference; etc.), and / or real-time operational data and historical service records bound to customer identity identifiers (the mapping relationship data and determination rule data can be configured by the administrator). This allows for regular access and querying of the customer identity impact coefficient corresponding to the customer identity identifier, the business base weight corresponding to the business type identifier, and the timeliness base weight corresponding to the business request time. Furthermore, the business request time can specifically be, but is not limited to, the time of receipt or initiation of the business request form (this initiation time can also be carried in the business request form).

[0036] In step S2, considering that in actual e-commerce customer service scenarios, customers' historical service records will reflect customers' personalized evaluation of service quality, in order to improve service quality, it is also necessary to include historical service records in the measurement of customer identity influence coefficient. That is, in order to improve the accuracy of customer identity influence coefficient, preferably, by querying multi-source data, the customer identity influence coefficient corresponding to the customer identity identifier is obtained, including but not limited to the following steps S21 to S23.

[0037] S21. Based on the customer identity identifier, obtain real-time operational data and historical service records corresponding to the customer identity identifier by querying multi-source data.

[0038] In step S21, the real-time operation data includes, but is not limited to, information such as the current customer level corresponding to the customer identity identifier, and the historical service records include, but are not limited to, information such as historical complaint events corresponding to the customer identity identifier. These can also be accessed and queried in a regular manner.

[0039] S22. Extract the current customer level corresponding to the customer identity identifier from the real-time operation data, and perform statistical analysis on the historical service records to obtain the number of historical complaints and / or the frequency of historical complaints corresponding to the customer identity identifier.

[0040] In step S22, since the historical service record includes, but is not limited to, information such as historical complaint events corresponding to the customer identity, the number of historical complaints and / or the frequency of historical complaints corresponding to the customer identity can be statistically obtained.

[0041] S23. Based on the customer's current level and the number of historical complaints and / or the frequency of historical complaints, determine the customer identity influence coefficient corresponding to the customer's identity identifier, wherein the customer identity influence coefficient is positively correlated with the customer's current level and also positively correlated with the number of historical complaints and / or the frequency of historical complaints.

[0042] In step S23, the positive correlation function between the customer identity influence coefficient and the customer's current level can be reflected by the aforementioned mapping relationship data between customer identity identifiers and customer identity influence coefficients. The positive correlation function between the customer identity influence coefficient and the number of historical complaints and / or the frequency of historical complaints can be obtained by the aforementioned determination rule data for the customer identity influence coefficient. Therefore, when querying multi-source data, the mapping relationship data and the determination rule data can also be obtained. Furthermore, by performing a conventional weighted calculation on the positive correlation results between the customer identity influence coefficient and the customer's current level, and the positive correlation results between the customer identity influence coefficient and the number of historical complaints and / or the frequency of historical complaints, the final customer identity influence coefficient corresponding to the customer identity identifier can be obtained.

[0043] Before step S2, in order to achieve flexible and convenient mapping relationships and determine the rule configuration, preferably, before querying multi-source data, the method also includes, but is not limited to, the following steps S201 to S203.

[0044] S201. Through a visual human-computer interaction interface, obtain the administrator's configuration results for the mapping relationship between business type identifier and business basic weight, the mapping relationship between customer identity identifier and customer identity influence coefficient, the determination rules for customer identity influence coefficient, the mapping relationship between time and timeliness basic weight, and / or the determination rules for handling urgency influence coefficient, etc.

[0045] In step S201, it is preferable to use the existing Vue+ElementUI components to realize the real-time interactive preview of the configuration, so as to further improve flexibility and convenience; for example, when adding the mapping relationship between the business type identifier and the basic business weight, the cost can be shortened from the current 3 person-days to 2 hours per person.

[0046] S202. Based on the two-way data binding technology of the front-end framework, a multi-dimensional comprehensive weight calculation engine is applied to calculate the multi-dimensional comprehensive weight under different configuration results, and the calculation results are previewed and displayed on the human-computer interaction interface. The multi-dimensional comprehensive weight calculation engine calculates the weights according to the following formula:

[0047] In the formula, This represents the result of the multidimensional comprehensive weight calculation. Indicates the basic weight of the business. This indicates the influence coefficient of customer identity. Indicates the timeliness-based weight. This indicates the urgency of the impact of the process.

[0048] In step S202, the two-way data binding technology is an existing core mechanism in the front-end framework that enables automatic synchronization between the data model and the view. This allows the view to update automatically when the model data changes, and the data to update synchronously when the user interacts with the view, thereby improving the flexibility and convenience of configuration operations. Furthermore, the multi-dimensional comprehensive weight calculation engine is used to comprehensively calculate the order weights from dimensions such as customer identity, business type, and real-time efficiency.

[0049] S203. The confirmed configuration results are stored using a decoupled JSON structure for access and query.

[0050] In step S203, the JSON (JavaScript Object Notation) structure is a lightweight data exchange structure that is based on JavaScript object notation and supports nesting and flexible data organization. Therefore, it has decoupling characteristics and is particularly suitable for storing the aforementioned mapping relationship data.

[0051] S3. Based on the customer identity impact coefficient, the basic business weight, the basic timeliness weight, and the urgency impact coefficient determined based on the absolute value of the time difference between the business request time and the business deadline and negatively correlated with that absolute value, a multi-dimensional comprehensive weight calculation engine is applied to calculate the multi-dimensional comprehensive weight of the business request form. The multi-dimensional comprehensive weight calculation engine calculates the weight according to the following formula:

[0052] In the formula, This represents the result of the multidimensional comprehensive weight calculation. Indicates the basic weight of the business. This indicates the influence coefficient of customer identity. Indicates the timeliness-based weight. This indicates the urgency of the impact of the process.

[0053] In step S3, the urgency impact coefficient is used to adjust the timeliness weight based on the urgency of processing. Specifically, the urgency impact coefficient can be determined, but is not limited to, in the following manner: when the absolute value of the time difference is less than or equal to a first time threshold, the urgency impact coefficient is determined. When the absolute value of the time difference is greater than the first time threshold and less than or equal to the second time threshold, the urgency impact coefficient is determined. Wherein, the second time threshold is greater than the first time threshold. and Represent pure decimals and have , This represents the absolute value of the time difference; when the absolute value of the time difference is greater than the second time threshold, the urgency impact coefficient is determined. The aforementioned first and second time thresholds can be pre-configured, and for example, the first time threshold can be 1 hour and the second time threshold can be 24 hours, as well as the aforementioned... and It can also be pre-configured, and examples can be given as follows: and .

[0054] S4. Based on the multi-dimensional comprehensive weight of the business request form, insert the business request form into the business request allocation queue, wherein the business request allocation queue includes at least one business request form to be allocated and arranged in descending order of weight.

[0055] In step S4, the business request form can be inserted between the preceding business request form whose current multidimensional comprehensive weight is greater than or equal to the multidimensional comprehensive weight of the business request form and the following business request form whose current multidimensional comprehensive weight is less than or equal to the multidimensional comprehensive weight of the business request form.

[0056] S5. Based on the current customer identity impact coefficient, the current timeliness base weight, and / or the current processing urgency impact coefficient, the multi-dimensional comprehensive weight calculation engine is used to dynamically reorder the business request order queue. The current timeliness base weight corresponds to the current time, and the current processing urgency impact coefficient is negatively correlated with the current absolute value of the time difference. The current absolute value of the time difference refers to the absolute value of the time difference between the current time and the business deadline.

[0057] In step S5, the dynamic reordering refers to first, for each business request in the business request queue, dynamically calculating the corresponding current multidimensional comprehensive weight based on the corresponding basic business weight, the current customer identity impact coefficient, the current timeliness basic weight, and / or the current processing urgency impact coefficient, using the multidimensional comprehensive weight calculation engine. Then, based on all calculation results, the business request queue is reordered sequentially in descending order of weight. Considering that customers' historical service records will increase over time (e.g., an increase in complaint incidents), it is necessary to update the customer identity impact coefficient based on the aforementioned steps S21-S23 to obtain the current customer identity impact coefficient for each business request. Also considering that for each business request, the current time can be considered as the corresponding new request time in real time, it is also necessary to obtain the timeliness basic weight corresponding to the current time by querying the multi-source data to obtain the current timeliness basic weight for each business request. Furthermore, the specific method for obtaining the current processing urgency impact coefficient can be found in the technical details of the aforementioned step S3, and will not be repeated here.

[0058] In step S5, to reduce the calls to the multi-dimensional comprehensive weight calculation engine during the dynamic reordering process to save computing resources and ensure that the business request orders at the top of the order are allocated in a timely manner, it is necessary to introduce a sliding window mechanism. This involves dynamically reordering the business request order queue based on the current customer identity impact coefficient, the current timeliness base weight, and / or the current processing urgency impact coefficient. This includes, but is not limited to, the following steps: When the first business request order in the business request order queue is allocated every unit of time, the following steps are used to sequentially traverse the multiple consecutive business request orders to be allocated in the business request order queue, selected sequentially from front to back using a sliding window: based on the currently traversed multiple consecutive business request orders... The current multidimensional comprehensive weight of the business request form is determined. It is then determined whether the difference in the current multidimensional comprehensive weight of each pair of adjacent business request forms in the currently traversed sequence is less than or equal to a preset weight threshold. If so, the current multidimensional comprehensive weight of the currently traversed sequence of business request forms is updated using the multidimensional comprehensive weight calculation engine based on the current customer identity impact coefficient, the current timeliness base weight, and / or the current processing urgency impact coefficient. Based on the update result, the currently traversed sequence of business request forms is rearranged in descending order of weight to dynamically reorder the business request order queue. Then, the next group of consecutive business request forms is traversed. Otherwise, the next group of consecutive business request forms is traversed directly. The width of the sliding window is... A business request form, The sliding window represents a positive integer greater than or equal to 2, and its sliding step size is... A business request form, Indicates less than or equal to A positive integer, the current timeliness base weight corresponds to the current time, the current urgency impact coefficient is negatively correlated with the current absolute value of the time difference, and the current absolute value of the time difference refers to the absolute value of the time difference between the current time and the business deadline. Therefore, through the sliding window, it can be traversed sequentially: [The following is a list of numbers and their corresponding values]. A series of consecutive business request orders, with sequence number as A series of consecutive business request orders, with sequence number as Multiple consecutive business request orders are processed sequentially. Furthermore, the unit duration can be pre-configured, for example, to be 1 minute; and the preset weight threshold can be, for example, 5.

[0059] S6. Any service request that is at the top of the service request queue after reordering will be assigned to the target service agent terminal.

[0060] In step S6, the target service agent terminal can be conventionally determined based on order allocation strategies such as round-robin or load balancing. To achieve strict security access control, preferably, the configuration permissions and / or viewing permissions of the order allocation strategy are mapped to the organizational structure tree through an RBAC model. This allows the mapping result to verify whether the user is within the scope of their organizational structure before the configuration and / or viewing operations. If so, the operation is permitted, and a corresponding operation log is recorded; otherwise, the operation is prohibited. The operation log includes, but is not limited to, the user's unique identifier, the operation timestamp, and a comparison of policy parameters before and after the operation. The RBAC (Role-Based Access Control) model is an access control mechanism that assigns permissions based on roles. Its core idea is to assign permissions to roles rather than directly to users, thereby simplifying access management. Therefore, it can complete the mapping with the organizational structure tree and achieve the permission verification purpose for configuration and / or viewing operations (experimental data shows that the permission mismatch rate is reduced to 0.3%). In addition, since the operation log records the unique identifier of the user, the operation timestamp, and the comparison results of policy parameters before and after the operation, operation auditing and historical backtracking can be performed at any time.

[0061] Therefore, based on the dynamic intelligent order allocation method described in steps S1 to S6 above, a new intelligent order allocation scheme is provided that dynamically calculates multi-dimensional weights and dynamically rearranges the order allocation queue based on customer identity, business type, and real-time timeliness. Specifically, after receiving a business request carrying a customer identity identifier, business type identifier, and business deadline, the system first queries multi-source data to obtain the customer identity influence coefficient corresponding to the customer identity identifier, the basic business weight corresponding to the business type identifier, and the basic timeliness weight corresponding to the business request time. Then, a multi-dimensional comprehensive weight calculation engine is applied to calculate the multi-dimensional comprehensive weight. The system first assigns weights to business request orders, then inserts these orders into the business request order allocation queue based on the weights. The system then uses a calculation engine to dynamically reorder the queue based on the current customer identity impact coefficient, timeliness base weight, and / or urgency impact coefficient. Finally, it assigns any business request order at the top of the queue to the target service agent terminal. This system provides multi-dimensional dynamic weight calculation capabilities and adaptability to real-time business changes, significantly improving the accuracy (experimental data shows that the accuracy rate increased from 72% to 94%) and efficiency of order allocation. This, in turn, improves service quality and customer experience, facilitating practical application and promotion.

[0062] like Figure 2As shown, the second aspect of this embodiment provides a virtual device for implementing the dynamic intelligent order allocation method described in the first aspect, including a service request receiving unit, a multi-source data query unit, a comprehensive weight calculation unit, a service request queue insertion unit, a queue dynamic rearrangement unit, and a service request allocation unit that are sequentially connected in communication. The business request receiving unit is used to receive a business request form carrying a customer identity identifier, a business type identifier, and a business deadline. The multi-source data query unit is used to obtain, based on the customer identity identifier, the business type identifier, and the business request time of the business request form, the customer identity influence coefficient corresponding to the customer identity identifier, the business basic weight corresponding to the business type identifier, and the timeliness basic weight corresponding to the business request time by querying multi-source data. The comprehensive weight calculation unit is used to calculate the multi-dimensional comprehensive weight of the business request form based on the customer identity impact coefficient, the business basic weight, the timeliness basic weight, and the processing urgency impact coefficient determined based on the absolute value of the time difference between the business request time and the business deadline and negatively correlated with the absolute value of the time difference. The multi-dimensional comprehensive weight calculation engine calculates the weight according to the following formula:

[0063] In the formula, This represents the result of the multidimensional comprehensive weight calculation. Indicates the basic weight of the business. This indicates the influence coefficient of customer identity. Indicates the timeliness-based weight. Indicates the urgency factor of the process; The business request queueing unit is used to insert the business request order into the business request allocation queue based on the multi-dimensional comprehensive weight of the business request order, wherein the business request allocation queue includes at least one business request order to be allocated and arranged in descending order of weight. The queue dynamic reordering unit is used to dynamically reorder the business request order queue based on the current customer identity impact coefficient, the current timeliness base weight, and / or the current processing urgency impact coefficient, using the multi-dimensional comprehensive weight calculation engine. The current timeliness base weight corresponds to the current time, and the current processing urgency impact coefficient is negatively correlated with the current absolute value of the time difference. The current absolute value of the time difference refers to the absolute value of the time difference between the current time and the business deadline. The service request allocation unit is used to allocate any service request that is at the top of the service request queue after reordering to the target service agent terminal.

[0064] The working process, working details and technical effects of the aforementioned device provided in the second aspect of this embodiment can be found in the dynamic intelligent order allocation method described in the first aspect, and will not be repeated here.

[0065] like Figure 3 As shown, the third aspect of this embodiment provides a computer device for executing the dynamic intelligent order allocation method as described in the first aspect. The device includes a storage module, a processing module, and a transceiver module connected in sequence. The storage module stores a computer program, the transceiver module sends and receives messages, and the processing module reads the computer program and executes the dynamic intelligent order allocation method as described in the first aspect. Specifically, the storage module may include, but is not limited to, random-access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; the processing module may, but is not limited to, use a microprocessor of the STM32F105 series. Furthermore, the computer device may also include, but is not limited to, a power supply module, a display screen, and other necessary components.

[0066] The working process, working details and technical effects of the aforementioned computer device provided in the third aspect of this embodiment can be found in the dynamic intelligent order allocation method described in the first aspect, and will not be repeated here.

[0067] This fourth aspect of the embodiment provides a computer-readable storage medium storing instructions comprising the dynamic intelligent order splitting method as described in the first aspect. Specifically, the computer-readable storage medium stores instructions that, when executed on a computer, perform the dynamic intelligent order splitting method as described in the first aspect. The computer-readable storage medium refers to a data storage medium, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or Memory Sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0068] The working process, working details and technical effects of the aforementioned computer-readable storage medium provided in the fourth aspect of this embodiment can be found in the dynamic intelligent order splitting method described in the first aspect, and will not be repeated here.

[0069] This fifth aspect of the embodiment provides a computer program product, including a computer program or instructions, which, when executed by a computer, implements the dynamic intelligent order allocation method as described in the first aspect. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0070] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A dynamic intelligent order allocation method based on multi-dimensional weights, characterized in that, include: Receive business request forms carrying customer identification, business type identification, and business deadline; Based on the customer identity identifier, the business type identifier, and the business request time of the business request form, by querying multi-source data, the customer identity influence coefficient corresponding to the customer identity identifier, the basic business weight corresponding to the business type identifier, and the basic timeliness weight corresponding to the business request time are obtained respectively. Based on the customer identity impact coefficient, the basic business weight, the basic timeliness weight, and the urgency impact coefficient determined by the absolute value of the time difference between the business request time and the business deadline and negatively correlated with that absolute value, a multi-dimensional comprehensive weight calculation engine is applied to calculate the multi-dimensional comprehensive weight of the business request form. The multi-dimensional comprehensive weight calculation engine calculates the weight according to the following formula: In the formula, This represents the result of the multidimensional comprehensive weight calculation. Indicates the basic weight of the business. This indicates the influence coefficient of customer identity. Indicates the timeliness-based weight. Indicates the urgency factor of the process; Based on the multi-dimensional comprehensive weight of the business request form, the business request form is inserted into the business request allocation queue, wherein the business request allocation queue includes at least one business request form to be allocated and arranged in descending order of weight. Based on the current customer identity impact coefficient, the current timeliness base weight, and / or the current processing urgency impact coefficient, the multi-dimensional comprehensive weight calculation engine is used to dynamically reorder the business request order queue. The current timeliness base weight corresponds to the current time, and the current processing urgency impact coefficient is negatively correlated with the current absolute value of the time difference. The current absolute value of the time difference refers to the absolute value of the time difference between the current time and the business deadline. Any business request that is at the top of the business request queue after reordering will be assigned to the target service agent terminal.

2. The dynamic intelligent order allocation method according to claim 1, characterized in that, Based on the customer identity identifier, the customer identity influence coefficient corresponding to the customer identity identifier is obtained by querying multi-source data, including: Based on the customer identification identifier, real-time operational data and historical service records corresponding to the customer identification identifier are obtained by querying multi-source data; Extract the current customer level corresponding to the customer identity identifier from the real-time operation data, and perform statistical analysis on the historical service records to obtain the number of historical complaints and / or the frequency of historical complaints corresponding to the customer identity identifier; Based on the customer's current level and the number and / or frequency of historical complaints, a customer identity influence coefficient corresponding to the customer's identity identifier is determined, wherein the customer identity influence coefficient is positively correlated with the customer's current level and also positively correlated with the number and / or frequency of historical complaints.

3. The dynamic intelligent order allocation method according to claim 1, characterized in that, The urgency factor for handling the matter is determined as follows: When the absolute value of the time difference is less than or equal to the first time threshold, the urgency impact coefficient is determined. ; When the absolute value of the time difference is greater than the first time threshold and less than or equal to the second time threshold, the urgency impact coefficient is determined. Wherein, the second time threshold is greater than the first time threshold. and Represent pure decimals and have , This represents the absolute value of the time difference; When the absolute value of the time difference is greater than the second time threshold, the urgency impact coefficient is determined. .

4. The dynamic intelligent order allocation method according to claim 1, characterized in that, Based on the current customer identity impact coefficient, the current timeliness base weight, and / or the current processing urgency impact coefficient, the multi-dimensional comprehensive weight calculation engine is used to dynamically reorder the business request order queue, including: When a business request is assigned every unit of time or when the first business request in the business request allocation queue is assigned, the following steps are performed: The following steps are taken to iterate through the consecutive business request queues to be assigned, selected sequentially from front to back using a sliding window: Based on the current multidimensional comprehensive weight of the currently iterated consecutive business request queues, it is determined whether the difference in the current multidimensional comprehensive weight of each pair of adjacent business request queues is less than or equal to a preset weight threshold. If so, the current multidimensional comprehensive weight of the currently iterated consecutive business request queues is updated using the multidimensional comprehensive weight calculation engine based on the current customer identity impact coefficient, the current timeliness base weight, and / or the current processing urgency impact coefficient. Based on the update result, the currently iterated consecutive business request queues are rearranged in descending order of weight to dynamically reorder the business request allocation queue. Then, the next group of consecutive business request queues is iterated through. Otherwise, the next group of consecutive business request queues is directly iterated through. The width of the sliding window is... A business request form, The sliding window represents a positive integer greater than or equal to 2, and its sliding step size is... A business request form, Indicates less than or equal to The positive integer, the current timeliness base weight corresponds to the current time, the current processing urgency impact coefficient is negatively correlated with the current time difference absolute value, and the current time difference absolute value refers to the absolute value of the time difference between the current time and the business deadline.

5. The dynamic intelligent order allocation method according to claim 1, characterized in that, Before querying multi-source data, the method further includes: Through a visual human-computer interaction interface, the administrator can obtain the configuration results of the mapping relationship between business type identifier and business basic weight, the mapping relationship between customer identity identifier and customer identity influence coefficient, the determination rules of customer identity influence coefficient, the mapping relationship between time and timeliness basic weight, and / or the determination rules of urgency influence coefficient. Based on the two-way data binding technology of the front-end framework, a multi-dimensional comprehensive weight calculation engine is applied to calculate the multi-dimensional comprehensive weight under different configuration results, and the calculation results are previewed and displayed on the human-computer interaction interface. The multi-dimensional comprehensive weight calculation engine calculates the weight according to the following formula: In the formula, This represents the result of the multidimensional comprehensive weight calculation. Indicates the basic weight of the business. This indicates the influence coefficient of customer identity. Indicates the timeliness-based weight. Indicates the urgency factor of the process; The confirmed configuration results are stored using a decoupled JSON structure for access and query.

6. The dynamic intelligent order allocation method according to claim 1, characterized in that, The target service agent terminal is determined based on the order allocation strategy. The configuration permissions and / or viewing permissions of the order allocation strategy are mapped to the organizational structure tree through the RBAC model. Before the configuration operation and / or viewing operation of the order allocation strategy, the mapping result is used to verify whether the operation user is within the scope of the operation user's organizational structure. If so, the operation is permitted and the corresponding operation log is recorded. Otherwise, the operation is prohibited. The operation log contains the unique identifier of the operation user, the operation timestamp, and the comparison results of the strategy parameters before and after the operation.

7. A dynamic intelligent order allocation device based on multi-dimensional weights, characterized in that, It includes a service request receiving unit with sequential communication connections, a multi-source data query unit, a comprehensive weight calculation unit, a service request queue insertion unit, a queue dynamic rearrangement unit, and a service request allocation unit; The business request receiving unit is used to receive a business request form carrying a customer identity identifier, a business type identifier, and a business deadline. The multi-source data query unit is used to obtain, based on the customer identity identifier, the business type identifier, and the business request time of the business request form, the customer identity influence coefficient corresponding to the customer identity identifier, the business basic weight corresponding to the business type identifier, and the timeliness basic weight corresponding to the business request time by querying multi-source data. The comprehensive weight calculation unit is used to calculate the multi-dimensional comprehensive weight of the business request form based on the customer identity impact coefficient, the business basic weight, the timeliness basic weight, and the processing urgency impact coefficient determined based on the absolute value of the time difference between the business request time and the business deadline and negatively correlated with the absolute value of the time difference. The multi-dimensional comprehensive weight calculation engine calculates the weight according to the following formula: In the formula, This represents the result of the multidimensional comprehensive weight calculation. Indicates the basic weight of the business. This indicates the influence coefficient of customer identity. Indicates the timeliness-based weight. Indicates the urgency factor of the process; The business request queueing unit is used to insert the business request order into the business request allocation queue based on the multi-dimensional comprehensive weight of the business request order, wherein the business request allocation queue includes at least one business request order to be allocated and arranged in descending order of weight. The queue dynamic reordering unit is used to dynamically reorder the business request order queue based on the current customer identity impact coefficient, the current timeliness base weight, and / or the current processing urgency impact coefficient, using the multi-dimensional comprehensive weight calculation engine. The current timeliness base weight corresponds to the current time, and the current processing urgency impact coefficient is negatively correlated with the current absolute value of the time difference. The current absolute value of the time difference refers to the absolute value of the time difference between the current time and the business deadline. The service request allocation unit is used to allocate any service request that is at the top of the service request queue after reordering to the target service agent terminal.

8. A computer device, characterized in that, It includes a storage module, a processing module, and a transceiver module that are sequentially connected in communication. The storage module is used to store computer programs, the transceiver module is used to send and receive messages, and the processing module is used to read the computer programs and execute the dynamic intelligent order allocation method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that... The computer-readable storage medium stores instructions that, when executed on a computer, perform the dynamic intelligent order splitting method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or the instructions are executed by the computer, they implement the dynamic intelligent order splitting method as described in any one of claims 1 to 6.

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