A warehouse data intelligent inventory method and system

Abstract

The present application discloses a warehouse data intelligent inventory system and method, which includes a warehouse intelligent system and a backend server, wherein the backend server is used to load a program to perform the following steps: according to a set data statistical time period, obtaining the number of occurrences of a first abnormal situation, wherein the first abnormal situation is determined based on monitoring and management data obtained from the warehouse intelligent system, and the start time node of the data statistical time period is the end time node of the previous inventory work; determining a first inventory prompt value based on the number of occurrences of the first abnormal situation; and outputting a first inventory warning signal when the first inventory prompt value is greater than a first warning threshold. By adopting the solution of the present application, the timeliness and rationality of the execution of the inventory work can be guaranteed, thereby improving the accuracy of the inventory work and the work processing efficiency, while also avoiding the increase of ineffective operating costs.

Description

Technical Field

[0001] The present invention relates to warehouse data analysis and processing technology, and in particular to a warehouse data intelligent inventory method and system. Background Art

[0002] Warehouse inventory counting is a crucial component of enterprise management. It not only ensures inventory accuracy, optimizes inventory management, and serves as data support for corporate decision-making, but also mitigates financial risks and enhances corporate image. Currently, warehouse inventory counting methods include two types: scheduled and irregular. Scheduled counts are generally conducted at predetermined times, such as at the end of the month, quarter, and / or year-end, while irregular counts are scheduled based on demand. However, the former suffers from a lack of flexibility. While the latter, while more flexible, relies heavily on warehouse managers' historical experience to determine whether an inventory count is necessary. Consequently, the frequency of inventory counting is heavily influenced by subjective factors and lacks reliable data to inform decision-making. This can lead to a mismatch between the frequency of inventory counting and actual needs. This can lead to poor accuracy due to untimely scheduling of inventory counting, or to inefficient warehouse operations and significantly increased operating costs due to over-scheduled inventory counting. Summary of the Invention

[0003] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application proposes a warehouse data intelligent inventory method and system.

[0004] In a first aspect, an embodiment of the present application provides a warehouse data intelligent inventory system, which includes a warehouse intelligent system and a backend server, wherein the warehouse intelligent system and the backend server are connected in communication and transmission, wherein:

[0005] The backend server includes at least one processor configured to load a program to execute the following steps:

[0006] S1. Obtain the number of occurrences of a first abnormality according to a set data statistical time period, wherein the first abnormality is determined based on monitoring management data obtained from a warehouse intelligent system, and the start time node of the data statistical time period is the end time node of the previous inventory work;

[0007] S2. Determine a first inventory reminder value based on the number of occurrences of the first abnormal situation;

[0008] S3. When the first inventory count prompt value is greater than the first warning threshold, output a first inventory count warning signal.

[0009] In some embodiments, step S2 specifically includes:

[0010] S201. Obtain the severity of each first abnormal situation;

[0011] S202: Determine a first weighted value corresponding to each impact severity; wherein the impact severity and the first weighted value are in direct proportion to each other;

[0012] S203: Determine a first inventory reminder sub-value corresponding to each first abnormal situation using the unit value and the first weighted value;

[0013] S204: Summing the first inventory reminder sub-values ​​corresponding to the plurality of first abnormal situations to obtain a first summation result, and determining a first inventory reminder value according to the first summation result.

[0014] In some embodiments, step S204 specifically includes:

[0015] S2041. Obtain several historical inventory error rates;

[0016] S2042. Calculate the average error rate of several historical inventory work errors to obtain an average error rate;

[0017] S2043. Obtain a second weighted value corresponding to the average error rate;

[0018] S2044: Summing the first inventory count prompt sub-values ​​corresponding to the plurality of first abnormal situations to obtain a first summation result, and determining a first inventory count prompt value according to the first summation result and the second weighted value.

[0019] In some embodiments, step S2043 specifically includes:

[0020] S20431. When the average value of the error rate is within a first allowable error range, obtain a first weighted sub-value, and use the first weighted sub-value as a second weighted value, wherein a lower limit of the first allowable error range is 0;

[0021] S20432: When the average value of the error rate is greater than the upper limit of the first allowable error range, increase the first weighted sub-value to obtain a second weighted sub-value, and use the second weighted sub-value as the second weighted value.

[0022] In some embodiments, step S2044 specifically includes:

[0023] S20441. Obtaining a product turnover rate within a statistical time period, wherein the product turnover rate is determined based on the total number of shipments and shipments within the statistical time period, and the total number of days within the statistical time period;

[0024] S20442. Determine a third weighted value corresponding to the product turnover rate, wherein the product turnover rate and the third weighted value are in direct proportion to each other;

[0025] S20443. Summing the first inventory reminder sub-values ​​corresponding to the plurality of first abnormal situations to obtain a first summation result, and determining a first inventory reminder value according to the first summation result, the second weighted value, and the third weighted value.

[0026] In some embodiments, the product turnover rate is determined by the following steps:

[0027] S20411. Calculate the second sum of the total number of outbound times and the total number of inbound times;

[0028] S20412. Calculate the division result between the second summation result and the total number of days;

[0029] S20413. Determine the product turnover rate based on the division result.

[0030] In some embodiments, the backend server is further configured to load a program to perform the following steps:

[0031] S4. When it is determined that the current inventory counting error rate is greater than the upper limit of the first allowable error range, the first abnormal situations are divided according to the inbound and outbound process nodes, and the first abnormal situations belonging to the same inbound and outbound process node are divided into the same first subset;

[0032] S5. Determine a first review priority value corresponding to the inbound and outbound process node based on the number of first abnormal situations included in the first subset;

[0033] S6. Arrange the first audit priority values ​​corresponding to the inbound and outbound process nodes in descending order.

[0034] In some embodiments, step S5 specifically includes:

[0035] S501. Determine a first audit priority value corresponding to an inbound and outbound process node according to the number of first abnormal situations included in the first subset and a first weighted value corresponding to each abnormal situation.

[0036] In some embodiments, the warehouse intelligence system includes:

[0037] Warehouse intelligent execution operation system, used for intelligent and automated execution of warehouse work;

[0038] Warehouse intelligent monitoring system, used to monitor warehouse scenes;

[0039] The semantic recognition system is used to perform semantic recognition on the input speech content.

[0040] In a second aspect, an embodiment of the present application provides a method for intelligent inventory of warehouse data, the method comprising the following steps:

[0041] S1. Obtain the number of occurrences of a first abnormality according to a set data statistical time period, wherein the first abnormality is determined based on monitoring management data obtained from a warehouse intelligent system, and the start time node of the data statistical time period is the end time node of the previous inventory work;

[0042] S2. Determine a first inventory reminder value based on the number of occurrences of the first abnormal situation;

[0043] S3. When the first inventory count prompt value is greater than the first warning threshold, output a first inventory count warning signal.

[0044] The present application can achieve at least one of the following technical effects: the present application scheme obtains the number of occurrences of the first abnormal situation according to the set data statistical time period, and then determines the first inventory prompt value based on the number of occurrences of the first abnormal situation. When the first inventory prompt value is greater than the first warning threshold, the first inventory warning signal is output. In this way, the greater the frequency of the abnormal situation, the faster the reminder arrangement of the inventory work will be triggered, and vice versa, the slower the reminder arrangement of the inventory work will be triggered. This not only ensures the timeliness of the execution of the inventory work, avoids the problem of low inventory accuracy due to untimely inventory work arrangement, but also avoids the problem of low warehouse operation efficiency and increased operating costs due to too frequent inventory work arrangement. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments.

[0046] Figure 1 A schematic diagram of the architecture of a warehouse data intelligent inventory system is provided for an embodiment of the present application;

[0047] Figure 2 A schematic diagram of the steps of a first embodiment of a method for intelligent inventory of warehouse data is provided for the embodiment of the present application;

[0048] Figure 3 A schematic flow chart of the steps of a second embodiment of a method for intelligent inventory of warehouse data is provided in accordance with an embodiment of the present application. DETAILED DESCRIPTION

[0049] To make the objectives, technical solutions, and advantages of this application more clear, the following will refer to the drawings in the embodiments of this application to clearly and completely describe the technical solutions of this application through implementation methods. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0050] According to the current warehouse management workflow, irregular inventory checks can be set up based on actual conditions, offering greater flexibility compared to regular inventory checks. However, the triggering of irregular inventory checks is generally based solely on the subjective judgment of senior staff members based on their experience. This approach is overly simplistic and fails to align well with actual conditions, leading to issues such as inventory checks being triggered too late or too frequently.

[0051] In order to solve the above situation, the applicant believes in the research process that it is necessary to use reliable and effective monitoring data as an effective decision-making basis, so that it can be more objective and more in line with the actual situation. Based on this, after analyzing the data of the current warehouse intelligent system, it is found that the more abnormal situations occur, the more likely it is to lead to low accuracy of inventory work, and even lower accuracy. For example, the abnormal situations that occur may at least include system network information difference leading to data packet loss or delayed data upload, execution equipment (such as AGV carts, sorting robotic arms) with abnormal execution time, abnormal movement trajectory, etc., and these abnormal situations will lead to errors in goods information recognition, incorrect placement, invalid grabbing of goods during sorting, delayed data upload, etc., which will cause the inventory data in the background warehouse management information to be inconsistent with the actual situation. Therefore, when the frequency of abnormal situations is large, the frequency of inventory work also needs to be larger. Conversely, the triggering frequency of inventory work can be reduced. Based on this, the embodiment of the present application provides a warehouse data intelligent inventory solution, which uses the frequency of abnormal situations as a decision-making basis to trigger inventory work.

[0052] Reference Figure 1 The present invention provides an intelligent warehouse data inventory system, which includes an intelligent warehouse system and a backend server. The intelligent warehouse system and the backend server are connected to each other through a communication transmission. The intelligent warehouse system and the backend server are described below.

[0053] The warehouse intelligent system is mainly used for intelligent automation and monitoring of the entire warehouse work. Specifically, the warehouse intelligent system may include at least the following subsystems:

[0054] Warehouse intelligent execution and operation systems, used for intelligent and automated execution of warehouse tasks; for example, they may include intelligent sorting systems (including cargo conveying systems) implemented using robotic arms, stackers, intelligent handling robots that can automatically place cargo from designated shelf locations onto transport trucks or vice versa, and unmanned automated transport trucks that only perform cargo handling, among other intelligent and automated execution equipment;

[0055] A warehouse intelligent monitoring system is used to monitor the warehouse scene; for example, it may include intelligent monitoring equipment such as cameras installed in the warehouse, unmanned aerial vehicles (with cameras) that can fly in the warehouse, environmental monitoring equipment (such as temperature and humidity monitoring equipment), and smoke monitoring equipment;

[0056] The semantic recognition system is used to perform semantic recognition on the input voice content; specifically, this system is mainly used to obtain the voice work log data recorded by the recording staff in the form of voice recording, and then use the semantic recognition deep learning model to identify and process the work log data to obtain the corresponding different named entity contents. Among them, based on the recognition of the named entity content, the abnormal work operation conditions involved in the voice work log data can be identified.

[0057] Of course, the above-mentioned warehouse intelligent system may also include other specific intelligent automation equipment, which will not be further elaborated here.

[0058] The background server includes at least one processor for loading a program to execute steps of a warehouse data intelligent inventory method.

[0059] Reference Figure 2 , an embodiment of the present application provides a method for intelligent inventory of warehouse data, which specifically includes the following steps.

[0060] S1. Obtain the number of occurrences of a first abnormal situation according to a set data statistical time period, wherein the first abnormal situation is determined based on monitoring management data obtained from a warehouse intelligent system, and the start time node of the data statistical time period is the end time node of the previous inventory work.

[0061] S2. Determine a first inventory reminder value based on the number of occurrences of the first abnormal situation;

[0062] S3. When the first inventory count prompt value is greater than the first warning threshold, output a first inventory count warning signal.

[0063] Specifically, if the inventory error rate doesn't meet the requirements after the inventory count, data analysis will be conducted to identify the influencing factors and make adjustments to bring the error rate within the requirements. Therefore, for the triggering of the (i+1)th inventory count, the main focus will be on counting the number of abnormalities that occurred after the i-th inventory count was completed. Therefore, the start time of the statistical period is the end time of the previous inventory count. When the first inventory count prompt value exceeds the first warning threshold, this moment is used as the end time of the statistical period.

[0064] The number of occurrences of the first abnormal situation is mainly determined by detecting the number of records of abnormal situations that are identified. When a new record of an abnormal situation is detected, the number of occurrences of the first abnormal situation is increased by 1; then, it is determined whether the first inventory prompt value determined based on the current number of occurrences exceeds the first warning threshold. If so, the first inventory warning signal is output to remind the staff to execute the inventory work arrangement as soon as possible. Otherwise, the records of abnormal situations continue to be detected. If a new record of an abnormal situation is detected, the process returns to the step of increasing the number of occurrences of the first abnormal situation by 1, and then determining whether the first inventory prompt value determined based on the current number of occurrences exceeds the first warning threshold, until the first inventory prompt value determined based on the current number of occurrences exceeds the first warning threshold.

[0065] The steps for determining the first abnormal situation specifically include: 1. obtaining the operating status information of the intelligent automation execution equipment from the warehouse intelligent execution operation system. When it is determined that the operating status information does not meet the preset requirements, it is determined that an abnormal situation has occurred. At this time, the first abnormal situation includes abnormal execution time, abnormal movement path, abnormal cargo grabbing, etc.; 2. obtaining the scene monitoring data monitored by the intelligent monitoring equipment from the warehouse intelligent monitoring system. When it is determined that the scene monitoring data does not meet the preset requirements, it is determined that an abnormal situation has occurred. At this time, the first abnormal situation includes goods being placed in an area where goods are not allowed to be placed, goods being placed on an open space for more than a preset time, and abnormal employee behavior / operation (for example, employee misoperation or employee illegal and irregular behavior). , abnormal environmental parameters (such as too high temperature may cause safety hazards or spoilage of goods; too high humidity may cause spoilage of goods, and changes in goods will lead to loss of goods. If the loss of goods is not recorded in time, it will easily increase the inventory error rate), etc.; 3. Obtain the named entity content in the voice work log data output by the semantic recognition system, and obtain the corresponding first abnormal situation after content recognition of the named entity content. For example, when the staff completes the corresponding work or performs work handover, they will use voice to record and report the work. Especially when an abnormal situation occurs, the staff will communicate with relevant personnel in the form of voice communication. At this time, these information data as voice work logs can be identified to identify the specific abnormal content of the abnormal situation. That is, for the monitoring and management data, it may include the operating status information of the intelligent automation execution equipment, the scene monitoring data obtained by the intelligent monitoring equipment, and the named entity content in the voice work log data.

[0066] It can be seen that the faster the frequency of abnormal situations, the faster the first inventory prompt value determined based on the number of occurrences of the first abnormal situation exceeds the first warning threshold, which means that the inventory work is triggered and arranged sooner, and vice versa, it is arranged later. This can avoid the problem of low accuracy of inventory work due to the occurrence of many abnormal situations but still no inventory work is arranged, and can also avoid the problem of low efficiency of overall warehouse operation and increased operating costs due to too frequent inventory work arrangements. In other words, by using the solution of the embodiment of the present application, the timeliness and rationality of triggering the execution of inventory work are improved, thereby improving the accuracy and efficiency of the inventory work, and reducing the ineffective loss of operating costs.

[0067] In some embodiments, different abnormal situations can be assigned different severity levels based on factors such as the location of the abnormal situation, the degree of difference between the abnormal situation and the normal situation, and the degree of negative impact on subsequent workflow nodes. The severity level can be expressed numerically, and the greater the severity of the abnormal situation, the sooner the inventory check should be triggered, thereby further ensuring the accuracy of the inventory check. Therefore, step S2 can specifically include the following steps.

[0068] S201. Obtain the severity of the impact of each first abnormal situation.

[0069] Specifically, step S201 may be specifically as follows: obtaining attribute parameters of the first abnormal situation as training input data, wherein the attribute parameters of the first abnormal situation may at least include the location where the first abnormal situation occurs, the time point when the abnormal situation occurs, the difference of the abnormal situation and / or the degree of negative impact on subsequent workflow nodes. Then, the training input data is input into the trained first deep learning training machine for processing, and the corresponding impact severity level number is output. The larger the value of the impact severity level number, the greater the impact severity.

[0070] S202. Determine a first weighted value k1 corresponding to each impact severity; wherein the impact severity and the first weighted value are in direct proportion, that is, the higher the impact severity level, the greater the first weighted value.

[0071] Specifically, in this embodiment, the first weighted value k1 may be greater than or equal to 1. Further preferably, the value range of the first weighted value may be between [a, b], where a is 1 and b is greater than or equal to 3. Alternatively, the specific value of b may be determined based on the number of levels of impact severity.

[0072] S203: Determine a first inventory reminder sub-value corresponding to each first abnormality using the unit value and the first weighted value. In this embodiment, the unit value may be 1, indicating that one more abnormality has been identified.

[0073] Specifically, the step S203 may be as follows: calculating the unit value M and the first weighted value k1 i+1 The product of the two, where k1 i+1 It is expressed as the first weighted value corresponding to the (i+1)th first abnormal situation, and the product result is used as the first inventory prompt sub-value corresponding to the (i+1)th first abnormal situation.

[0074] S204: Summing the first inventory reminder sub-values ​​corresponding to the plurality of first abnormal situations to obtain a first summation result, and determining a first inventory reminder value according to the first summation result.

[0075] Specifically, since the unit value is 1, the first inventory prompt sub-values ​​corresponding to the first abnormal situations are summed to obtain a first summation result. At this time, the first summation result I i+1 That is, it is equal to k11+k12+…+k1 i+1 , where k11 represents the first weighted value corresponding to the first abnormal situation, k12 represents the first weighted value corresponding to the second abnormal situation, and so on. i+1 The first weighted value corresponding to the (i+1)th abnormality is equivalent to summing the first weighted values ​​corresponding to each detected first abnormality. Furthermore, the first inventory reminder value determined based on the first summation result can be used directly as the first inventory reminder value, or the first summation result can be further processed to obtain a result that is then used as the first inventory reminder value.

[0076] It can be seen that this embodiment adjusts the first inventory prompt sub-value corresponding to each abnormal situation by adding the factor of the severity of the impact of the abnormal situation, thereby adjusting the first inventory prompt value, so that the triggering of the inventory work is more closely matched with the abnormal frequency and the severity of the impact, thereby further ensuring the accuracy of the inventory work.

[0077] In some embodiments, generally, the more frequent the inventory counts, that is, the shorter the time intervals between inventory counts, the higher the accuracy of the work, and even the ability to maintain a zero error rate. Therefore, by identifying and judging the error rates of historical inventory counts, it can be determined whether the triggering speed of the inventory counts needs to be adjusted. For example, if the error rate of historical inventory counts is high, the inventory counts need to be triggered more quickly; conversely, there is no need to speed up the triggering of the inventory counts. In fact, based on considerations of warehouse work efficiency, the triggering of the inventory counts can even be slowed down. Therefore, step S204 can specifically include the following steps.

[0078] S2041. Obtain several historical inventory error rates.

[0079] Specifically, the historical inventory error rate refers to the original inventory error rate obtained after the inventory count, that is, the unmodified error rate (because if the error rate is found to be greater than the set threshold, it will generally be modified through cause analysis to ensure that the final error rate meets the requirements). In addition, the selection of the historical inventory error rate can be set according to actual needs. In this embodiment, in order to better fit the actual situation, the selection method is to select the first 10 historical inventory error rates. For example, if the current inventory count is the 11th, then the error rates corresponding to the 1st to 10th historical inventory counts are obtained.

[0080] S2042. Calculate the average error rate of several historical inventory work errors to obtain an average error rate. Specifically, using the average error rate as a basis for determining the subsequent second weighted value will result in higher data accuracy and closer to the actual situation.

[0081] S2043. Obtain a second weighted value corresponding to the average error rate.

[0082] Specifically, the step S2043 may include the following steps.

[0083] S20431. When the average error rate value is within a first allowable error range, obtain a first weighted sub-value, and use the first weighted sub-value as a second weighted value, wherein the lower limit of the first allowable error range is 0, that is, [0, c].

[0084] S20432. When the average error rate is greater than the upper limit c of the first allowable error range, the first weighted sub-value is increased to obtain a second weighted sub-value, and the second weighted sub-value is used as the second weighted value. This approach allows for expediting the triggering of an inventory count by increasing the weighted value of the first summation result when the historical error rate does not meet the accuracy requirement, thereby reducing the error rate of the next triggered inventory count until it meets the requirement.

[0085] Furthermore, the first difference between the second weighted sub-value and the first weighted sub-value is directly proportional to the second difference between the average error rate and the upper limit c of the first allowable error range. This means that the greater the degree to which the average error rate exceeds the specified accuracy requirement, the sooner the inventory check must be triggered. Furthermore, since the first difference represents the degree of increase in the first weighted sub-value, and considering that too frequent inventory check triggering increases operating costs, the maximum value of the first difference is constrained by the increase in operating costs. Specifically, the maximum value of the first difference is determined based on the increase in operating costs, ensuring that the increase in operating costs does not exceed the specified budget while shortening the duration of the inventory check trigger.

[0086] S2044: Summing the first inventory count prompt sub-values ​​corresponding to the plurality of first abnormal situations to obtain a first summation result, and determining a first inventory count prompt value according to the first summation result and the second weighted value.

[0087] Specifically, the first inventory prompt value is determined based on the first summation result and the second weighted value. The first product result between the first summation result and the second weighted value k2 is calculated, that is, k2*I i+1 , and use the first product result as the first inventory prompt value.

[0088] In some embodiments, if the goods are highly mobile, i.e., the frequency of goods entering and leaving the warehouse is high, then this indicates that the inventory changes rapidly and the number of times goods enter and leave the warehouse is high. This can easily lead to a larger error rate in the inventory count. Therefore, the time required to trigger the inventory count should be shortened. Therefore, step S2044 may specifically include the following steps.

[0089] S20441. Obtain the goods turnover rate within the data statistical time period, wherein the goods turnover rate is determined based on the total number of outbound shipments, the total number of inbound shipments, and the total number of days in the data statistical time period.

[0090] S20442. Determine a third weighted value k3 corresponding to the product turnover rate, wherein the product turnover rate and the third weighted value are in direct proportion.

[0091] S20443. Summing the first inventory reminder sub-values ​​corresponding to the plurality of first abnormal situations to obtain a first summation result, and determining a first inventory reminder value according to the first summation result, the second weighted value, and the third weighted value.

[0092] Specifically, the first inventory reminder value is determined based on the first summation result, the second weighted value k2, and the third weighted value k3. The second product of the first summation result, the second weighted value k2, and the third weighted value k3 is calculated, that is, k3*k2*I i+1 , using the second product as the first inventory count reminder value. This allows us to adjust the trigger duration of the inventory count based on the severity of the impact, the error rate, and the flow of goods, further tailoring it to actual conditions. While meeting the specific requirements of the severity of the abnormal situation, the inventory count error rate, and the flow of goods, we can improve inventory count accuracy while ensuring work efficiency and operating costs.

[0093] In some embodiments, in order to improve the accuracy of determining the product turnover rate, the product turnover rate is determined by the following steps:

[0094] S20411. Calculate the second sum of the total number of outbound times and the total number of inbound times;

[0095] S20412. Calculate the division result between the second summation result and the total number of days, that is, division result = (total number of shipments + total number of shipments) / total number of days;

[0096] S20413. Determine the product turnover rate based on the division result.

[0097] Specifically, in this embodiment, the division result can be directly used as the product turnover rate, or the division result can be adjusted using the empirical weighted value k4, and the adjusted result can be used as the product turnover rate, that is, the product turnover rate at this time is k4*division result.

[0098] In some embodiments, reference Figure 3 , this method embodiment also includes the following steps.

[0099] S4. When it is determined that the current inventory counting error rate is greater than the upper limit of the first allowable error range, the first abnormal situations are divided according to the inbound and outbound process nodes, and the first abnormal situations belonging to the same inbound and outbound process node are divided into the same first subset.

[0100] Specifically, when the error rate of the current inventory work exceeds the preset range, it is necessary to conduct an inventory analysis of the process of entering and leaving the warehouse to find out the reasons for the excessive error rate and make subsequent modifications. Based on the above content, it can be concluded that the abnormal situation is the main factor causing the error rate. Therefore, all first abnormal situations can be divided according to the entry and exit process nodes as the basis for division, so that the first abnormal situations belonging to the same entry and exit process node are divided into the same first subset. Among them, based on the location of the occurrence of the first abnormal situation, the time point of occurrence, the corresponding execution operation, etc., it can be determined at which entry and exit process node the first abnormal situation occurs.

[0101] S5. Determine a first audit priority value corresponding to the inbound and outbound process node according to the number of first abnormal situations included in the first subset.

[0102] S6. Arrange the first audit priority values ​​corresponding to the inbound and outbound process nodes in order from large to small. This way, the corresponding inbound and outbound process nodes can be checked and audited in order from large to small based on the number of occurrences / number of abnormal situations. This makes it easier and faster to check and audit problems, greatly improving work processing efficiency.

[0103] In some embodiments, the method embodiment further includes the following steps.

[0104] S7. For the first abnormal situations included in the first subset, divide the first abnormal situations according to their occurrence locations, so that the first abnormal situations belonging to the same location area are divided into the same second subset;

[0105] S8. Determine second audit priority values ​​corresponding to different location area ranges based on the number of first abnormal situations included in the second subset;

[0106] S9. Arrange the second audit priority values ​​corresponding to the location area ranges in order from large to small. In this way, during the investigation and audit of any inbound and outbound process node, the investigation can be started from the location area range where the most abnormal situations occur, thereby further improving the efficiency of the investigation and audit processing work.

[0107] In some embodiments, step S5 specifically includes:

[0108] S501. Determine a first audit priority value corresponding to an inbound and outbound process node according to the number of first abnormal situations included in the first subset and a first weighted value corresponding to each abnormal situation.

[0109] Specifically, the specific implementation method of this step is: after summing the first weighted numerical values ​​corresponding to each first abnormal situation contained in the first subset, the summation result is determined as the first audit priority value corresponding to the first subset, and one inbound and outbound process node corresponds to one first subset.

[0110] Similarly, the step S8 may specifically include:

[0111] S801. Determine the second audit priority values ​​corresponding to different location area ranges based on the number of first abnormal situations included in the second subset and the first weighted value corresponding to each abnormal situation.

[0112] Similarly, the specific implementation method of this step is: after summing the first weighted values ​​corresponding to each first abnormal situation contained in the second subset, the summation result is determined as the second audit priority value corresponding to the second subset, and a location area range corresponds to a second subset.

[0113] In addition, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the above method embodiment.

[0114] For the processors mentioned in the above storage medium embodiment and system embodiment, the number can be at least one, and at least any step in the above method embodiment can be executed. When the number is at least two, at least two processors can be connected to each other for communication, not limited to wired or wireless communication connection, and the at least one processor can be connected to various intelligent terminal devices for communication. In addition, the processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0115] Finally, it should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0116] Note that the above are only preferred embodiments of the present application and the technical principles employed. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments and may include many other equivalent embodiments without departing from the scope of the present application. The scope of the present application is determined by the scope of the appended claims.

Claims

1. A warehouse data intelligent inventory system, characterized in that: The system includes a warehouse intelligent system and a backend server, wherein the warehouse intelligent system and the backend server are connected in communication and transmission, wherein: The backend server includes at least one processor configured to load a program to execute the following steps: S1. Obtain the number of occurrences of a first abnormality according to a set data statistical time period, wherein the first abnormality is determined based on monitoring management data obtained from a warehouse intelligent system, and the start time node of the data statistical time period is the end time node of the previous inventory work; S2. Determine a first inventory reminder value based on the number of occurrences of the first abnormal situation; The step S2 specifically includes: S201, obtaining the impact severity of each first abnormal situation; S202, determining a first weighted value corresponding to each impact severity; wherein the impact severity and the first weighted value are in direct proportion; S203, determining a first inventory reminder sub-value corresponding to each first abnormal situation using the unit value and the first weighted value; S204, summing the first inventory reminder sub-values ​​corresponding to a plurality of first abnormal situations to obtain a first summation result, and determining a first inventory reminder value based on the first summation result; Step S204 specifically includes: S2041, obtaining a plurality of historical inventory error rates; S2042, calculating an average value of the plurality of historical inventory error rates to obtain an average error rate; S2043, obtaining a second weighted value corresponding to the average error rate; S2044, summing a plurality of first inventory prompt sub-values ​​corresponding to the first abnormal conditions to obtain a first summation result, and determining a first inventory prompt value based on the first summation result and the second weighted value; The step S2044 specifically includes: S20441, obtaining a product turnover rate within a data statistical time period, wherein the product turnover rate is determined based on the total number of times goods are shipped out of the warehouse, the total number of times goods are received in the warehouse, and the total number of days in the data statistical time period; S20442, determining a third weighted value corresponding to the product turnover rate, wherein the product turnover rate and the third weighted value are in a positive proportional relationship; S20443, summing first inventory reminder sub-values ​​corresponding to a plurality of first abnormal situations to obtain a first summation result, and determining a first inventory reminder value based on the first summation result, the second weighted value, and the third weighted value; S3. When the first inventory count prompt value is greater than the first warning threshold, output a first inventory count warning signal.

2. The system according to claim 1, wherein The step S2043 specifically includes: S20431. When the average value of the error rate is within a first allowable error range, obtain a first weighted sub-value, and use the first weighted sub-value as a second weighted value, wherein a lower limit of the first allowable error range is 0; S20432: When the average value of the error rate is greater than the upper limit of the first allowable error range, increase the first weighted sub-value to obtain a second weighted sub-value, and use the second weighted sub-value as the second weighted value.

3. The system according to claim 1, wherein: The product turnover rate is determined by the following steps: S20411. Calculate the second sum of the total number of outbound times and the total number of inbound times; S20412. Calculate the division result between the second summation result and the total number of days; S20413. Determine the product turnover rate based on the division result.

4. The system according to claim 2, wherein: The backend server is further configured to load a program to perform the following steps: S4. When it is determined that the current inventory counting error rate is greater than the upper limit of the first allowable error range, the first abnormal situations are divided according to the inbound and outbound process nodes, and the first abnormal situations belonging to the same inbound and outbound process node are divided into the same first subset; S5. Determine a first review priority value corresponding to the inbound and outbound process node based on the number of first abnormal situations included in the first subset; S6. Arrange the first audit priority values ​​corresponding to the inbound and outbound process nodes in descending order.

5. The system according to claim 4, wherein: The step S5 specifically includes: S501. Determine a first audit priority value corresponding to an inbound and outbound process node according to the number of first abnormal situations included in the first subset and a first weighted value corresponding to each abnormal situation.

6. The system according to any one of claims 1 to 5, wherein: The warehouse intelligent system includes: a warehouse intelligent execution operation system for intelligently and automatically executing warehouse work; Warehouse intelligent monitoring system, used to monitor warehouse scenes; The semantic recognition system is used to perform semantic recognition on the input speech content.

7. A method for intelligent inventory of warehouse data, characterized in that: The method comprises the following steps: S1. Obtain the number of occurrences of a first abnormality according to a set data statistical time period, wherein the first abnormality is determined based on monitoring management data obtained from a warehouse intelligent system, and the start time node of the data statistical time period is the end time node of the previous inventory work; S2. Determine a first inventory reminder value based on the number of occurrences of the first abnormal situation; The step S2 specifically includes: S201, obtaining the impact severity of each first abnormal situation; S202, determining a first weighted value corresponding to each impact severity; wherein the impact severity and the first weighted value are in direct proportion; S203, determining a first inventory reminder sub-value corresponding to each first abnormal situation using the unit value and the first weighted value; S204, summing the first inventory reminder sub-values ​​corresponding to a plurality of first abnormal situations to obtain a first summation result, and determining a first inventory reminder value based on the first summation result; Step S204 specifically includes: S2041, obtaining a plurality of historical inventory error rates; S2042, calculating an average value of the plurality of historical inventory error rates to obtain an average error rate; S2043, obtaining a second weighted value corresponding to the average error rate; S2044, summing a plurality of first inventory prompt sub-values ​​corresponding to the first abnormal conditions to obtain a first summation result, and determining a first inventory prompt value based on the first summation result and the second weighted value; The step S2044 specifically includes: S20441, obtaining a product turnover rate within a data statistical time period, wherein the product turnover rate is determined based on the total number of times goods are shipped out of the warehouse, the total number of times goods are received in the warehouse, and the total number of days in the data statistical time period; S20442, determining a third weighted value corresponding to the product turnover rate, wherein the product turnover rate and the third weighted value are in a positive proportional relationship; S20443, summing first inventory reminder sub-values ​​corresponding to a plurality of first abnormal situations to obtain a first summation result, and determining a first inventory reminder value based on the first summation result, the second weighted value, and the third weighted value; S3. When the first inventory count prompt value is greater than the first warning threshold, output a first inventory count warning signal.

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