System control method based on RPA technology

Abstract

The invention discloses a system control method based on an RPA technology, and the method comprises the steps: obtaining a to-be-processed order in a current period, and analyzing the to-be-processed order to obtain a corresponding order label; obtaining a matching degree between the order label of the to-be-processed order and the processing capability label of the system processing capability; and when the matching degree is greater than a first preset threshold value, automatically receiving the order according to a preset RPA script. By applying the scheme, personalized automatic order receiving can be realized.

Description

Technical Field

[0001] The present invention relates to the field of RPA technology, and more specifically to a system control method based on RPA technology. Background Art

[0002] RPA (Robotic Process Automation) is a technology that uses software robots to simulate human operations on computers to automate business processes. It can automatically perform repetitive and regular tasks such as data input, file processing, report generation, etc., thereby freeing employees' hands, improving work efficiency and reducing errors.

[0003] However, in the existing technology, RPA technology can usually only process data according to preset processes and can only identify whether the data belongs to the processing object, but cannot flexibly process the data according to the type or value of the data. Therefore, when the system using RPA technology changes, RPA needs to be reconfigured. However, configuring RPA requires professionals and involves code work, which is relatively complicated. Therefore, the existing technology has the problem of insufficient flexibility. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a system control method based on RPA technology to flexibly process pending orders.

[0005] The present invention solves the above technical problems through the following technical solutions: The present invention provides a system control method based on RPA technology, the method comprising: Obtain pending orders in the current cycle, and parse the pending orders to obtain corresponding order tags; Obtaining the matching degree between the order tag of the pending order and the processing capacity tag of the system processing capacity; When the degree of matching is greater than a first preset threshold, the order is automatically accepted according to the preset RPA script.

[0006] Optionally, obtaining the matching degree between the order tag of the to-be-processed order and the processing capability tag of the system processing capability includes: Obtaining order tags corresponding to pending orders, wherein the order tags include: business type information of pending orders and processing indicator information of pending orders; sorting the business type information of pending orders as constituent elements to obtain a first sequence; Acquire a processing capability label of the system processing capability, the processing capability label including: business type information of the processing capability and processing indicator information of the processing capability; sort the business type information of the processing capability as a component element to obtain a second sequence; The elements in the first sequence are mapped to the elements in the second sequence using a semantic similarity algorithm to obtain the number of target elements in the first sequence mapped to the second sequence, obtain the ratio of the number of target elements to the number of elements in the first sequence, and use the ratio as the degree of matching.

[0007] Optionally, mapping the elements in the first sequence with the elements in the second sequence by using a semantic similarity algorithm to obtain the number of target elements mapped between the first sequence and the second sequence includes: Sequentially, matching the current element in the first sequence with each element in the second sequence one by one in terms of semantic similarity, and determining the semantic similarity between the current element in the first sequence and each element in the second sequence; When the semantic similarity is greater than a second preset threshold, the current element is taken as the target element.

[0008] Optionally, the method further includes: Obtain the first order automatically accepted by RPA from the pending orders, classify and count the first order according to preset statistical indicators, and obtain a first statistical result, wherein the statistical indicators include: order type, processing object, and processing indicator information, and the order types include: power meter delivery order, transformer delivery order, collection terminal delivery order, power meter installation order, transformer installation order, and collection terminal installation order; According to the first statistical result and the preset alarm rule, it is determined whether the alarm condition is met, and if so, an alarm is output.

[0009] Optionally, the method further includes: The second orders automatically accepted by RPA in the historical period are obtained, and the second orders are classified and counted according to the preset statistical indicators to obtain the second statistical results; The consistency between the first statistical result and the second statistical result is calculated, and if the consistency is lower than a third preset threshold, an alarm is output.

[0010] Optionally, the method further includes: Calculate the number of orders to be processed per unit time and processing index information based on the first statistical result and the distribution period of the orders to be processed; According to the number of pending orders within the unit time and the duration of the next cycle, the total number of orders under each order type corresponding to the future set time period is calculated, and the task volume for processing the total number of orders is determined according to the processing indicator information; The total number of orders and the task volume are output as demand forecast information.

[0011] Optionally, the method further includes: Determine the target quantity of equipment to be consumed and the current inventory quantity according to the total number of orders, and output an alarm of insufficient inventory when the target quantity is greater than or equal to the inventory quantity; return to execute the step of obtaining pending orders when the target quantity is less than the inventory quantity; and / or, The target working hours required are determined based on the task volume. When the target working hours are greater than or equal to the maximum available working hours, an alarm indicating that the task volume is saturated is output; when the target working hours are less than the maximum available working hours, the step of obtaining pending orders is returned to be executed.

[0012] Optionally, the method further includes: Obtaining the demand forecast result for the current cycle in the previous cycle, wherein the demand forecast result includes: the total number of orders predicted for the current cycle and the task volume predicted for the current cycle; The consistency between the demand forecast result and the first statistical result of the current period is determined, and if the consistency is lower than a fourth preset threshold, an alarm is output.

[0013] Optionally, the method further includes: According to the processing progress of the pending orders counted by the first statistical result, the pending order lines are classified and counted according to the processing progress to obtain and display the third statistical result.

[0014] Optionally, the method further includes: When the matching degree is less than or equal to the first preset threshold, the third order that was not automatically accepted by the RPA and abandoned in the historical period is obtained; the order label corresponding to the third order is obtained, and the business type information of the third order is sorted as a component element to obtain a third sequence; Using a semantic similarity algorithm, the elements in the third sequence are mapped to the elements in the second sequence to obtain a mapping relationship between the elements in the first sequence and the elements in the second sequence; Taking the element corresponding to the mapping relationship in the second sequence as the first mapping element, and obtaining the business step corresponding to the first mapping element; A second mapping element corresponding to the mapping relationship is obtained, the business steps are sorted according to the relationship of the second mapping element in the third sequence to obtain a target business sequence, and a third sequence of business processing steps is generated according to the target business sequence.

[0015] Compared with the prior art, the present invention has the following advantages: The present invention identifies the order tags of pending orders, uses the order tags to describe the pending orders, then matches the pending orders with the system, and determines whether to accept the pending orders based on the matching results. Even if deployed to different systems, only the processing capacity information of the system needs to be modified without reconfiguring the RPA process, which reduces time consumption, improves the flexibility of the system, and can adapt to different types of systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A first flow chart of a system control method based on RPA technology provided in an embodiment of the present invention; Figure 2 A second flow chart of the system control method based on RPA technology provided in an embodiment of the present invention; Figure 3 A third flow chart of the system control method based on the RPA technology provided in an embodiment of the present invention; Figure 4 A fourth flow chart of the system control method based on RPA technology provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given, but the protection scope of the present invention is not limited to the following embodiment.

[0018] Figure 1 A first flow chart of a system control method based on RPA technology provided in an embodiment of the present invention is as follows: Figure 1 As shown, the method includes: S101: Obtain pending orders in the current cycle, and parse the pending orders to obtain corresponding order tags.

[0019] In actual applications, the orders to be processed include electricity meter delivery orders, transformer delivery orders, data collection terminal delivery orders, electricity meter installation orders, transformer installation orders, and data collection terminal installation orders. In addition, some types of orders have different installation or delivery requirements, that is, different processing indicator information, so the system needs to allocate corresponding processing capabilities.

[0020] Taking the electric energy meter delivery order as an example, the business type information of the pending order in the order tag of the pending order is: electric energy meter delivery order; the processing indicator information of the pending order in the order tag of the pending order is delivery distance 50km; delivery time: 10:00:00. Therefore, the corresponding first sequence can be obtained by sequentially arranging the above information as elements.

[0021] S102: Obtaining the matching degree between the order tag of the order to be processed and the processing capability tag of the system processing capability.

[0022] Similarly, the processing capacity tag of the system processing capacity is obtained. The processing capacity tag of the system may contain multiple types of business type information of the processing capacity, such as electric energy meter delivery orders, transformer delivery orders, collection terminal delivery orders, electric energy meter installation orders, transformer installation orders, collection terminal installation orders, etc. There may also be many types of processing indicator information of the processing capacity, such as delivery distance, delivery time, etc. Then, a similar sorting order is used to sort the business type information of the processing capacity as a component element to obtain the second sequence.

[0023] Then, the elements in the first sequence are mapped to the elements in the second sequence using a semantic similarity algorithm. For example, if the business type information of the order to be processed is an electricity meter delivery order, and the business type information of the processing capability is an electricity meter transportation order, then the semantic similarity between the two is greater than the second preset threshold; if the business type information of the processing capability is an electricity meter installation order, then the semantic similarity between the two is less than the second preset threshold. Whether these words match can be pre-configured by the user.

[0024] If the processing index information of the pending order is the delivery distance of 50km, and the processing index information of the processing capacity is the delivery distance of 100km, then the two have the same semantics, and the elements in the first sequence (business type information of the pending order) are mapped with the elements in the second sequence (processing index information of the processing capacity), and the elements in the first sequence (business type information of the pending order) are the target elements; if the processing index information of the pending order is the delivery distance of 50km, and the processing index information of the processing capacity is the delivery distance of 30km, and the processing index information of the pending order exceeds the upper limit of the processing index information of the processing capacity, then the two have different semantics, and the elements in the first sequence (business type information of the pending order) and the elements in the second sequence (processing index information of the processing capacity) cannot be mapped, and the elements in the first sequence (business type information of the pending order) are not the target elements. In this way, the number of target elements mapped in the first sequence and the second sequence can be obtained, and the ratio of the number of target elements to the number of elements in the first sequence can be obtained, and the ratio is used as the matching degree. The higher the ratio of target elements, the higher the matching degree between the first sequence and the second sequence.

[0025] Of course, in practical applications, the cosine similarity between the first sequence and the second sequence can also be calculated. Of course, the Euclidean distance between the first sequence and the second sequence can also be calculated, and the embodiment of the present invention is not limited here, and the similarity between the two can be obtained.

[0026] S103: When the matching degree is greater than a first preset threshold, the order is automatically accepted according to a preset RPA script.

[0027] When the cosine similarity is high, it means that the system's processing capacity can handle pending orders. Therefore, the RPA script interface is called to automatically accept orders using RPA. In this way, sub-item functions such as grabbing orders for electric energy meters, grabbing orders for transformers, and grabbing orders for data collection terminals can be realized.

[0028] In order to realize the real-time monitoring and early warning of pending orders, such as order submission monitoring and early warning, order grabbing monitoring and early warning, order review monitoring and early warning and other sub-functions. Figure 2 A second flow chart of the system control method based on RPA technology provided in an embodiment of the present invention is as follows: Figure 2 As shown, in Figure 1 After step S103, the method further includes: S201: Obtain the first order automatically accepted by RPA from the pending orders, classify and count the first order according to preset statistical indicators, and obtain a first statistical result, wherein the statistical indicators include: order type, processing object, processing indicator information, and the order types include: electricity meter delivery order, transformer delivery order, collection terminal delivery order, electricity meter installation order, transformer installation order, and collection terminal installation order.

[0029] Taking order grabbing monitoring and early warning as an example, the system counts different types of pending orders according to preset statistical indicators. Each type of pending order corresponds to order type, processing object, processing indicator information and other information, and then obtains the first statistical result of each type of pending order in different dimensions (order type, processing object, processing indicator information). For example, the first statistical result shows that there are 5 electricity meter delivery orders and the delivery distance is greater than or equal to 50km.

[0030] S202: Determine whether an alarm condition is met based on the first statistical result and a preset alarm rule, and if so, output an alarm.

[0031] If the preset alarm rule is that the delivery distance cannot be greater than 40km, then since the delivery distance of the pending orders obtained by RPA automatic order acceptance is greater than or equal to 50km, an alarm is output at this time.

[0032] By using the above embodiment of the present invention, the user can configure some alarm rules, and then issue early warnings for pending orders within the system processing capacity according to these alarm rules, thereby reducing excessive waste or over-allocation of processing capacity.

[0033] Figure 3 A third flow chart of the system control method based on RPA technology provided in an embodiment of the present invention is as follows: Figure 3 As shown, in Figure 2After step S202, the method further includes: S301: Obtain the second order automatically accepted by the RPA in the historical period, classify and count the second orders according to preset statistical indicators, and obtain a second statistical result.

[0034] In actual applications, one week is taken as a cycle, and the historical period includes several cycles before the current cycle. Then, the pending orders automatically accepted by RPA in the historical period are extracted, and then statistics are performed according to the method described in S201 to obtain the second statistical result.

[0035] S302: Calculate the consistency between the first statistical result and the second statistical result, and output an alarm if the consistency is lower than a third preset threshold.

[0036] Specifically, the consistency judgment method for business type information can be implemented using the semantic similarity method in step S102. For the delivery distance in the processing index information, the difference between the two can be calculated. When the difference is greater than the set difference, it is determined that the processing index information in the first statistical result is inconsistent with the processing index information in the second statistical result. When the difference is less than the set difference, it can be determined that the processing index information in the first statistical result is consistent with the processing index information in the second statistical result. Similarly, if there are many delivery distances in the first statistical result, then the average value of the delivery distances in the first statistical result is calculated, and then the average value of the delivery distances in the second statistical result is calculated, and the difference between the two average values ​​is calculated.

[0037] If the consistency judgment results of the business type information and the consistency judgment results of the processing index information are both negative, it is determined that the consistency is lower than the third preset threshold, and an alarm is output.

[0038] By applying the embodiment of the present invention, the alarm is output through consistency judgment, so that the current cycle of abnormal automatic order acceptance can be identified, thereby realizing the supervision and management of automatic order acceptance.

[0039] Figure 4 A fourth flow chart of a system control method based on RPA technology provided in an embodiment of the present invention is shown in FIG. Figure 4 As shown, in Figure 2 After step S202, the method further includes: S401: Calculate the number of orders to be processed and processing index information per unit time according to the first statistical result and the distribution period of the orders to be processed.

[0040] For example, in the current cycle, 70 electricity meter delivery orders were automatically accepted within 7 days, which means an average of 10 orders per day.

[0041] Similarly, the average delivery distance of 70 electricity meter delivery orders is 20 km / order, so the processing indicator information corresponding to the first statistical result is: delivery distance 20 km / order.

[0042] S402: Calculate the total number of orders under each order type corresponding to a future set period based on the number of orders to be processed within the unit time and the duration of the next cycle, and determine the task volume for processing the total number of orders based on the processing indicator information.

[0043] If the next cycle is 10 days long, the total number of orders for the next cycle is 10 pieces / day*10 days=100 pieces; it can be understood that if there are multiple order types, the total number of orders includes orders of different order types.

[0044] The task volume corresponding to the processing index information is 20 km / order. In other words, the processing index information in the first statistical result can be directly used as the task volume for the next cycle.

[0045] S403: Output the total number of orders and the task quantity as demand forecast information.

[0046] In actual applications, demand forecast information mainly includes sub-functions such as estimated installation demand statistics for the next cycle of new installations, estimated installation demand statistics for the next cycle of failures, and estimated installation demand statistics for the next cycle of rotations.

[0047] Distribution demand (task volume) forecast: mainly includes sub-functions such as electricity meter distribution demand forecast, transformer distribution demand forecast, and data collection terminal distribution demand forecast.

[0048] By applying the above-mentioned embodiment of the present invention, the total number of orders and the task volume in the next cycle can be predicted.

[0049] Furthermore, the method further comprises: Determine the target quantity of equipment to be consumed and the current inventory quantity according to the total number of orders, and output an alarm of insufficient inventory when the target quantity is greater than or equal to the inventory quantity; return to execute the step of obtaining pending orders when the target quantity is less than the inventory quantity; and / or, The target working hours required are determined based on the task volume. When the target working hours are greater than or equal to the maximum available working hours, an alarm indicating that the task volume is saturated is output; when the target working hours are less than the maximum available working hours, the step of obtaining pending orders is returned to be executed.

[0050] Recommended inventory statistics at the end of the next cycle: mainly includes sub-item functions such as inventory statistics at the end of the next cycle for electricity meters, inventory statistics at the end of the next cycle for transformers, and inventory statistics at the end of the next cycle for data collection terminals.

[0051] Inventory asset statistics: mainly includes electricity meter inventory asset statistics, transformer inventory asset statistics, collection terminal inventory asset statistics, and remaining installation quantity statistics at the end of the new installation period.

[0052] By applying the above-mentioned embodiments of the present invention, inventory warning can be implemented to avoid the impact of insufficient inventory on business.

[0053] Furthermore, the method further comprises: Obtaining the demand forecast result for the current cycle in the previous cycle, wherein the demand forecast result includes: a forecast value of the total number of orders for the current cycle and a forecast value of the task volume for the current cycle; The consistency between the demand forecast result and the first statistical result of the current period is determined, and if the consistency is lower than a fourth preset threshold, an alarm is output.

[0054] For example, the previous cycle of the current cycle predicted that 70 electricity meter delivery orders were automatically accepted in the current cycle, and the average delivery distance of the 70 electricity meter delivery orders was 20km / order; but in fact, 80 electricity meter delivery orders were automatically accepted in the current cycle, and the average delivery distance of the 70 electricity meter delivery orders was 25km / order. If the difference between the actual number of automatically accepted orders in the current cycle and the predicted number of automatically accepted orders in the current cycle is within the set range, and the difference between the actual delivery distance in the current cycle and the predicted delivery distance in the current cycle is within the set range, it can be judged that the demand forecast result is consistent with the first statistical result of the current cycle. If the difference between one of the two is not within the set range, it can be judged that the demand forecast result is inconsistent with the first statistical result of the current cycle.

[0055] Demand forecast deviation analysis: mainly includes sub-functions such as electricity meter demand forecast deviation analysis, transformer demand forecast deviation analysis, and data collection terminal demand forecast deviation analysis.

[0056] By applying the above-mentioned embodiments of the present invention, it is possible to implement execution deviation warning, timely discover inaccurate prediction rules, and then make improvements to facilitate better business development.

[0057] In another embodiment of the present invention, the method further comprises: For example, in the past month or the past quarter, if the number of orders that RPA cannot accept exceeds the set number or exceeds the set proportion of the total order quantity due to the system's inability to process orders, then step A is executed.

[0058] A: When the degree of matching is less than or equal to the first preset threshold, that is, when the result of step S102 is no, obtain the third order in the historical period that was not automatically accepted by RPA and was abandoned; obtain the order tag corresponding to the third order, obtain the business type information in the order tag of the third order, and sort the business steps corresponding to the business type information as constituent elements to obtain a third sequence.

[0059] For example, the third sequence of order tags corresponding to the third order includes: ABCDEF, where each English letter is an order label.

[0060] It should be emphasized that the business steps corresponding to each business type are pre-configured. For example, the business types corresponding to the electricity meter installation business include electricity meter delivery, electricity meter transportation, electricity meter installation, and electricity meter installation business verification. The business steps of the electricity meter delivery business type include inventory query, delivery process submission, and delivery.

[0061] B: Map the elements in the third sequence with the elements in the second sequence using a semantic similarity algorithm to obtain a mapping relationship between each element in the first sequence and the second sequence.

[0062] Similarly, the order label of the second sequence is eABfCDGH. Then a mapping relationship is established between each element in the first sequence and the second sequence. At this time, A corresponds to A, E corresponds to e, F corresponds to f, and so on.

[0063] C: Take the element corresponding to the mapping relationship in the second sequence as the first mapping element, and obtain the business step corresponding to the first mapping element; the first mapping element is eABCDf, and its corresponding business steps are: E, A, B, C, D, and F.

[0064] D: Obtain the second mapping element corresponding to the mapping relationship, sort the business steps according to the relationship of the second mapping element in the third sequence to obtain a target business sequence, and generate a third sequence of business processing steps according to the target business sequence.

[0065] Then, according to the order of the third sequence "ABCDEF", sort E, A, B, C, D, and F to obtain the business sequence of A, B, C, D, E, and F, and use this business sequence as the target business sequence.

[0066] By applying the above embodiments of the present invention, the system can automatically create a target business sequence, reducing the workload of creating new business logic. In actual applications, the target business sequence can be displayed to the user, and the user can decide whether to start the target business sequence. After the user enters an instruction to start the target business sequence, the target business sequence is started. In this way, the system's processing capacity can actually handle it, but the RPA makes an inaccurate judgment, thereby avoiding the erroneous rejection of orders.

[0067] Furthermore, the above process is applicable to the case where each element in the third sequence can find a mapping element in the second sequence, and the case where each element in the third sequence may not find a mapping element in the second sequence. For the elements in the third sequence that cannot find a mapping element in the second sequence, corresponding blank business steps can be generated.

[0068] For example, in the third sequence "ABCZDEF", "Z" is an element in the third sequence that does not find a mapping element in the second sequence. Therefore, after sorting "E Jia Yi Bing Ding Ji", a blank business step is inserted between "Bing Ding" in the obtained "A Yi Bing Ding Wu Ji", thereby obtaining a target business sequence. The blank business step is manually configured by the user for a second time, and the above embodiment of the present invention can be applied to the creation of a target business sequence when an element in the third sequence that does not find a mapping element in the second sequence is found.

[0069] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A system control method based on RPA technology, characterized in that: The method comprises: Obtain pending orders in the current cycle, and parse the pending orders to obtain corresponding order tags; Obtaining the matching degree between the order tag of the pending order and the processing capacity tag of the system processing capacity; When the degree of matching is greater than a first preset threshold, the order is automatically accepted according to the preset RPA script.

2. The system control method based on RPA technology according to claim 1 is characterized in that: The obtaining of the matching degree between the order tag of the to-be-processed order and the processing capability tag of the system processing capability includes: Obtaining order tags corresponding to pending orders, wherein the order tags include: business type information of pending orders and processing indicator information of pending orders; sorting the business type information of pending orders as constituent elements to obtain a first sequence; Acquire a processing capability label of the system processing capability, the processing capability label including: business type information of the processing capability and processing indicator information of the processing capability; sort the business type information of the processing capability as a component element to obtain a second sequence; The elements in the first sequence are mapped to the elements in the second sequence using a semantic similarity algorithm to obtain the number of target elements in the first sequence mapped to the second sequence, obtain the ratio of the number of target elements to the number of elements in the first sequence, and use the ratio as the degree of matching.

3. The system control method based on RPA technology according to claim 2 is characterized in that: The using of a semantic similarity algorithm to map the elements in the first sequence with the elements in the second sequence to obtain the number of target elements mapped between the first sequence and the second sequence includes: Sequentially, matching the current element in the first sequence with each element in the second sequence one by one in terms of semantic similarity, and determining the semantic similarity between the current element in the first sequence and each element in the second sequence; When the semantic similarity is greater than a second preset threshold, the current element is taken as the target element.

4. The system control method based on RPA technology according to claim 1 is characterized in that: The method further comprises: Obtain the first order automatically accepted by RPA from the pending orders, classify and count the first order according to preset statistical indicators, and obtain a first statistical result, wherein the statistical indicators include: order type, processing object, and processing indicator information, and the order types include: power meter delivery order, transformer delivery order, collection terminal delivery order, power meter installation order, transformer installation order, and collection terminal installation order; According to the first statistical result and the preset alarm rule, it is determined whether the alarm condition is met, and if so, an alarm is output.

5. The system control method based on RPA technology according to claim 4 is characterized in that: The method further comprises: The second orders automatically accepted by RPA in the historical period are obtained, and the second orders are classified and counted according to the preset statistical indicators to obtain the second statistical results; The consistency between the first statistical result and the second statistical result is calculated, and if the consistency is lower than a third preset threshold, an alarm is output.

6. The system control method based on RPA technology according to claim 4 is characterized in that: The method further comprises: Calculate the number of orders to be processed per unit time and processing index information based on the first statistical result and the distribution period of the orders to be processed; According to the number of pending orders within the unit time and the duration of the next cycle, the total number of orders under each order type corresponding to the future set time period is calculated, and the task volume for processing the total number of orders is determined according to the processing indicator information; The total number of orders and the task volume are output as demand forecast information.

7. The system control method based on RPA technology according to claim 6 is characterized in that: The method further comprises: Determine the target quantity of equipment to be consumed and the current inventory quantity according to the total number of orders, and output an alarm of insufficient inventory when the target quantity is greater than or equal to the inventory quantity; return to execute the step of obtaining pending orders when the target quantity is less than the inventory quantity; and / or, The target working hours required are determined based on the task volume. When the target working hours are greater than or equal to the maximum available working hours, an alarm indicating that the task volume is saturated is output; when the target working hours are less than the maximum available working hours, the step of obtaining pending orders is returned to be executed.

8. The system control method based on RPA technology according to claim 4 is characterized in that: The method further comprises: Obtaining the demand forecast result for the current cycle in the previous cycle, wherein the demand forecast result includes: the total number of orders predicted for the current cycle and the task volume predicted for the current cycle; The consistency between the demand forecast result and the first statistical result of the current period is determined, and if the consistency is lower than a fourth preset threshold, an alarm is output.

9. The system control method based on RPA technology according to claim 4 is characterized in that: The method further comprises: According to the processing progress of the pending orders counted by the first statistical result, the pending order lines are classified and counted according to the processing progress to obtain and display the third statistical result.

10. The system control method based on RPA technology according to claim 2, characterized in that: The method further comprises: When the matching degree is less than or equal to the first preset threshold, the third order that was not automatically accepted by the RPA and abandoned in the historical period is obtained; the order label corresponding to the third order is obtained, and the business type information of the third order is sorted as a component element to obtain a third sequence; Using a semantic similarity algorithm, the elements in the third sequence are mapped to the elements in the second sequence to obtain a mapping relationship between the elements in the first sequence and the elements in the second sequence; Taking the element corresponding to the mapping relationship in the second sequence as the first mapping element, and obtaining the business step corresponding to the first mapping element; A second mapping element corresponding to the mapping relationship is obtained, the business steps are sorted according to the relationship of the second mapping element in the third sequence to obtain a target business sequence, and a third sequence of business processing steps is generated according to the target business sequence.

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