Data processing method and device, equipment, storage medium and program product

Abstract

The invention provides a data processing method which can be applied to the technical field of computers, the technical field of warehouse logistics, the technical field of artificial intelligence and the technical field of big data. The data processing method comprises: in response to receiving a target value determination request for M business departments, obtaining a reference target value of a target area, the target area comprising the M business departments; for the ith business department in the M business departments, acquiring basic information data of the ith business department and historical data generated by the ith business department in a preset time period to obtain ith target data; generating an ith predicted target value of the ith business department according to the ith target data to obtain M predicted target values; and optimizing the M predicted target values according to the reference target value and a predetermined constraint condition, and displaying respective optimized target values of the M business departments. The invention further provides a data processing device, equipment, a storage medium and a program product.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computer, the technical field of warehouse logistics, the technical field of artificial intelligence, and the technical field of big data, and more particularly, to a data processing method and device, equipment, a storage medium, and a program product. BACKGROUND

[0002] In the technical field of warehouse logistics, each branch office manages daily logistics work such as collection, dispatch, and distribution through a logistics system. The logistics system includes a target value module, and each branch office manually inputs recommended target values into the logistics system. The target values and actual completion situations are displayed in the logistics system to provide a reference for the work progress and arrangement of the branch office, and to monitor the target value achievement of the branch office.

[0003] In the process of implementing the present disclosure, the inventors found that at least the following problems exist in the related art: The input of the recommended target value is manually input by a person, and the deviation of personal understanding and the degree of familiarity with the business can cause missing information in the input, which can easily cause deviation and repeated input of the target value; in addition, the recommended target value is usually set based on expert experience, and since the number of branch offices is large and the scale, personnel, and other conditions are different, the efficiency and accuracy of target value setting are low. SUMMARY

[0004] Therefore, the present disclosure provides a data processing method and device, equipment, a storage medium, and a program product.

[0005] One aspect of the present disclosure provides a data processing method, including: in response to receiving a target value determination request for M branch offices, obtaining a reference target value of a target region, wherein the target region includes M branch offices, and M is a positive integer greater than or equal to 1; for an i-th branch office of the M branch offices, obtaining basic information data of the i-th branch office and historical data generated by the i-th branch office in a preset time period from a database to obtain i-th target data, wherein 1≤i≤M; generating an i-th predicted target value of the i-th branch office according to the i-th target data to obtain M predicted target values; and optimizing the M predicted target values according to the reference target value and a predetermined constraint condition to display an optimized target value of each of the M branch offices.

[0006] According to the embodiments of the present disclosure, optimizing the M predicted target values according to the reference target value and the predetermined constraint condition to display the optimized target value of each of the M branch offices includes: for a j-th predicted target value of the M predicted target values, determining a j-th optimization interval according to the j-th predicted target value and a predetermined optimization rule to obtain M optimization intervals, wherein 1≤j≤M; and determining the optimized target value of each of the M branch offices from the M optimization intervals according to the M optimization intervals, the reference target value, and the predetermined constraint condition.

[0007] According to an embodiment of the present disclosure, the determining, from the M optimization intervals, the M optimization target values of the M branch offices respectively according to the M optimization intervals, the reference target value and the predetermined constraint condition comprises: determining, from the jth optimization interval, a jth candidate interval satisfying the predetermined constraint condition to obtain M candidate intervals; determining, for each of the M candidate intervals, a candidate value satisfying a target function from the candidate interval to obtain M candidate values, wherein the target function is to minimize the deviation between the sum of the M candidate values and the reference target value; and determining the M candidate values as the M optimization target values of the M branch offices respectively.

[0008] According to an embodiment of the present disclosure, the method further comprises: in response to a change operation for the reference target value, updating the target function according to the changed reference target value.

[0009] According to an embodiment of the present disclosure, the determining, for each of the M candidate intervals, the candidate value satisfying the target function from the candidate interval to obtain the M candidate values comprises: determining, for each of the M candidate intervals, the candidate value satisfying the updated target function from the candidate interval to obtain the M candidate values.

[0010] According to an embodiment of the present disclosure, the method further comprises: for the ith branch office, obtaining target branch office information within a predetermined distance from the ith branch office; and adjusting the ith predicted target value according to the target branch office information.

[0011] According to an embodiment of the present disclosure, the target branch office information comprises the number of target branch offices.

[0012] According to an embodiment of the present disclosure, the adjusting the ith predicted target value according to the target branch office information comprises: in a case where the number of target branch offices is greater than or equal to a predetermined threshold, adjusting the ith predicted target value downward according to a preset proportion.

[0013] According to an embodiment of the present disclosure, the method further comprises: in a case where the ith target data comprises target value adjustment data, determining a target value adjustment frequency according to the target value adjustment data; determining a target value fluctuation rule of the ith branch office according to the target value adjustment frequency; and adjusting the ith predicted target value according to the target value fluctuation rule.

[0014] According to an embodiment of the present disclosure, the method further comprises: sending the respective optimization target values to the logistics systems of the M branch offices respectively; receiving a target value adjustment request sent by the logistics system of the ith branch office, wherein the target value adjustment request comprises a pre-adjustment value; and in a case where the pre-adjustment value satisfies a predetermined condition, sending the pre-adjustment value to the logistics system of the ith branch office.

[0015] Another aspect of the present disclosure provides a data processing apparatus, comprising: a first obtaining module, in response to receiving a target value determination request for M business branches, configured to obtain a reference target value of a target region, wherein the target region comprises M business branches, and M is a positive integer greater than or equal to 1; a second obtaining module, configured to obtain, for an i-th business branch in the M business branches, basic information data of the i-th business branch and historical data generated by the i-th business branch in a preset time period, to obtain i-th target data, wherein 1≤i≤M; a generating module, configured to generate an i-th predicted target value of the i-th business branch according to the i-th target data, to obtain M predicted target values; and an optimizing module, configured to optimize the M predicted target values according to the reference target value and a predetermined constraint condition, and display an optimized target value of each of the M business branches.

[0016] Another aspect of the present disclosure provides an electronic device, comprising: one or more processors; a memory configured to store one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method as described above.

[0017] Another aspect of the present disclosure provides a computer-readable storage medium, storing computer-executable instructions, wherein the instructions, when executed, are configured to implement the method as described above.

[0018] Another aspect of the present disclosure provides a computer program product, comprising computer-executable instructions, wherein the instructions, when executed, are configured to implement the method as described above.

[0019] According to the embodiments of the present disclosure, the target data of the business branch including the basic information data and the historical data is obtained from the database, the predicted target value of the business branch is generated according to the target data, the predicted target value is optimized according to the reference target value and the predetermined constraint condition, and the optimized target value of the business branch is displayed. By comprehensively considering the historical information and the basic information of the business branch, the target value of the business branch is predicted, and then the target value is further optimized by referring to the reference target value and the constraint condition, so as to improve the accuracy and rationality of the recommended target value in the logistics system, and to mine the internal relationship between the historical information and the basic information of the business branch and the target value of the business branch. Meanwhile, the scheme automatically generates the target value, thereby reducing the manual input. BRIEF DESCRIPTION OF DRAWINGS

[0020] The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure taken in conjunction with the accompanying drawings, in which:

[0021] Figure 1 An exemplary system architecture of the data processing method, apparatus, device, storage medium and program product according to the embodiments of the present disclosure is schematically shown;

[0022] Figure 2 a flowchart of a data processing method according to an embodiment of the present disclosure is schematically shown;

[0023] Figure 3 a flowchart of obtaining M branch office respective optimization target values according to an embodiment of the present disclosure is schematically shown;

[0024] Figure 4 a flowchart of determining M branch office respective optimization target values according to another embodiment of the present disclosure is schematically shown;

[0025] Figure 5 a flowchart of a data processing method according to another embodiment of the present disclosure is schematically shown;

[0026] Figure 6 a block diagram of a data processing apparatus according to an embodiment of the present disclosure is schematically shown; and

[0027] Figure 7 a block diagram of an electronic device suitable for implementing a data processing method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0028] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood, however, that the description which follows is merely exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description of embodiments of the present disclosure, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it would be apparent to one skilled in the art that one or more embodiments of the present disclosure can be practiced without these specific details. In other instances, descriptions of well-known structures and techniques have been omitted in order to avoid obscuring the concepts of the present disclosure.

[0029] The terms used herein are merely used to describe specific embodiments and are not intended to limit the present disclosure. The terms "include", "comprise", and the like used herein indicate the presence of the described features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0030] All terms used herein (including technical and scientific terms) have meanings commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having meanings consistent with the context of the present specification, and should not be interpreted in an idealized or overly formal manner.

[0031] In the case of using expressions similar to "at least one of A, B, and C, etc.", it is generally intended to include any of A, B, and C alone, or a combination of A, B, and C, etc.

[0032] In embodiments of the present disclosure, the collection, updating, analysis, processing, use, transmission, provision, disclosure, storage, etc. of the data involved (for example, including but not limited to user personal information) comply with relevant laws and regulations, are used for legal purposes, and do not violate public order and good customs. In particular, necessary measures are taken for user personal information to prevent illegal access to user personal information data, and to maintain user personal information security, network security and national security.

[0033] In embodiments of the present disclosure, the authorization or consent of the user is obtained before the user's personal information is acquired or collected.

[0034] In the field of warehouse logistics technology, each logistics branch office manages daily pickup, dispatch, distribution and other logistics work through a logistics system. The logistics system includes a target value module, and each branch office manually inputs the recommended target value into the logistics system. The target value and actual completion are displayed in the logistics system, providing a reference for the work progress and arrangement of the branch office, and monitoring the target value achievement of the branch office.

[0035] In the related technical solutions, the input source of the recommended target value is manual input, and the deviation of personal understanding and the degree of familiarity with the business will cause the missing information of the input, which is easy to cause the deviation and repeated input of the target value.

[0036] In addition, the recommended target value is usually set based on expert experience. Since the number of logistics branch offices is large, and the scale, level, personnel structure and road fence of the branch offices are different, a large amount of time and manpower is needed for data collection and analysis work, which is easy to cause low efficiency and omissions caused by manual experience judgment, and the efficiency and accuracy of target value setting are low.

[0037] In summary, in the related solutions, the setting of the target value of the logistics branch office is performed manually, and the efficiency and accuracy are low.

[0038] To solve the above problems, an embodiment of the present disclosure provides a data processing method, comprising: in response to receiving a target value determination request for M business branches, obtaining a reference target value of a target region, wherein the target region comprises M business branches, and M is a positive integer greater than or equal to 1; for an i-th business branch in the M business branches, obtaining basic information data of the i-th business branch and historical data generated by the i-th business branch in a preset time period from a database to obtain i-th target data, wherein 1≤i≤M; generating an i-th predicted target value of the i-th business branch according to the i-th target data to obtain M predicted target values; and optimizing the M predicted target values according to the reference target value and a predetermined constraint condition, and displaying an optimized target value of each of the M business branches.

[0039] Figure 1 An exemplary system architecture 100 to which the data processing method, apparatus, device, storage medium and program product according to the embodiments of the present disclosure can be applied is schematically shown. It should be noted that, Figure 1 The system architecture shown is only an example of the system architecture to which the embodiments of the present disclosure can be applied, to help those skilled in the art understand the technical content of the present disclosure, but does not mean that the embodiments of the present disclosure cannot be applied to other devices, systems, environments or scenarios.

[0040] As Figure 1 shown, the system architecture 100 according to the embodiment can include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104 and a server 105. The network 104 is a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103 and the server 105. The network 104 can include various connection types, such as wired and / or wireless communication links, etc.

[0041] A user can use the first terminal device 101, the second terminal device 102, the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients and / or social platform software, etc. (only as examples).

[0042] The first terminal device 101, the second terminal device 102, the third terminal device 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers and desktop computers, etc.

[0043] The server 105 can be a server that provides various services, such as a background management server (as an example only) that provides support for a website browsed by a user using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server can perform analysis and the like on received user requests and the like, and feed back a processing result (such as a web page, information, or data obtained or generated according to a user request) to the terminal device.

[0044] It should be noted that the data processing method provided by the embodiments of the present disclosure can generally be executed by the server 105. Accordingly, the data processing system provided by the embodiments of the present disclosure can generally be arranged in the server 105. The data processing method provided by the embodiments of the present disclosure can also be executed by a server or a server cluster different from the server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or the server 105. Accordingly, the data processing system provided by the embodiments of the present disclosure can also be arranged in a server or a server cluster different from the server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or the server 105. Alternatively, the data processing method provided by the embodiments of the present disclosure can also be executed by the first terminal device 101, the second terminal device 102, or the third terminal device 103, or by another terminal device different from the first terminal device 101, the second terminal device 102, or the third terminal device 103. Accordingly, the data processing system provided by the embodiments of the present disclosure can also be arranged in the first terminal device 101, the second terminal device 102, or the third terminal device 103, or in another terminal device different from the first terminal device 101, the second terminal device 102, or the third terminal device 103.

[0045] For example, the reference target value of the target region and the i-th target data can originally be stored in any one of the terminal devices 101, 102, or 103 (for example, the terminal device 101, but not limited thereto), or on an external storage device and can be imported into the terminal device 101. Then, the terminal device 101 can execute the data processing method provided by the embodiments of the present disclosure locally, or transmit the reference target value of the target region and the i-th target data to another terminal device, a server, or a server cluster, and execute the data processing method provided by the embodiments of the present disclosure by the other terminal device, the server, or the server cluster that receives the reference target value of the target region and the i-th target data.

[0046] It should be understood that Figure 1 The number of terminal devices, networks, and servers in the system shown in FIG. 1 is merely illustrative. Any number of terminal devices, networks, and servers can be provided as needed.

[0047] Figure 2 A flowchart of a data processing method according to an embodiment of the present disclosure is shown schematically.

[0048] As shown in Figure 2 The method includes operations S210-S240.

[0049] In operation S210, in response to receiving a target value determination request for M branches, a reference target value of a target region is obtained, wherein the target region includes M branches, and M is a positive integer greater than or equal to 1.

[0050] According to an embodiment of the present disclosure, each branch can be equipped with a respective terminal device, and a target value determination request for the branch can be sent to a server through the terminal device.

[0051] According to an embodiment of the present disclosure, the management department of the target region can also be equipped with a terminal device, and a target value determination request for the branches in the target region can be sent to the server by the management department of the target region through the terminal device.

[0052] According to an embodiment of the present disclosure, the target value determination request can carry basic information data and historical data of the branch.

[0053] According to an embodiment of the present disclosure, the branch can be a logistics branch.

[0054] According to an embodiment of the present disclosure, the target value can be a monthly target value, a quarterly target value, an annual target value, etc. The time span of the target value is not limited in the embodiments of the present disclosure.

[0055] According to an embodiment of the present disclosure, the target value can include a logistics order type target value, a customer experience type target value, and an organization service evaluation type target value. The logistics order type target value can include order quantity, order sending quantity, order picking quantity, and income. The customer experience type target value can include pickup and collection rate, departure rate, delivery rate, loss rate, and service score. The organization service evaluation type target value can be employee satisfaction and employee turnover rate.

[0056] According to an embodiment of the present disclosure, the target region can be a specified region determined according to actual management needs. For example, it can be a province, a city, a district, etc., or multiple provinces, multiple cities, multiple districts, etc. The range of the target region is not limited in the embodiments of the present disclosure.

[0057] According to an embodiment of the present disclosure, the reference target value can be an expected value of the M branches in the target region, which can be set by the management department managing the branches in the target region.

[0058] According to an embodiment of the present disclosure, the reference target value can include a sum of target values of the M branches, or can include target values of the M branches respectively.

[0059] According to an embodiment of the present disclosure, the branch in the target region can be one or multiple. Embodiments of the present disclosure do not limit the number of branches.

[0060] In operation S220, for the i-th branch of the M branches, basic information data of the i-th branch and historical data generated by the i-th branch in a preset time period are obtained from the database to obtain i-th target data, where 1≤i≤M.

[0061] According to an embodiment of the present disclosure, the database can include an information table, and the information table can include identification information, basic information data and historical data of different branches. In the process of obtaining the basic information data of the i-th branch and the historical data generated by the i-th branch in the preset time period from the database, the identification information of the i-th branch can be found, and the basic information data of the i-th branch and the historical data generated by the i-th branch in the preset time period can be obtained according to the identification information to obtain the i-th target data.

[0062] According to an embodiment of the present disclosure, the basic information data can include geographic location information of the branch, personnel information of the branch, road area fence information of the business, pickup range of the branch, main business of the branch, first cooperation time of the branch, etc. The geographic location information of the branch can include latitude and longitude of the branch, province and city to which the branch belongs, etc. The personnel information of the branch can include personnel structure and number, etc. The road area fence information of the business can include road fence situation and community fence situation of the road area where the branch is located. The main business of the branch can include cold chain logistics, fresh food logistics, large item logistics, small item logistics, etc. The first cooperation time of the branch can include the time when the branch first cooperates with the platform.

[0063] According to an embodiment of the present disclosure, the historical data can include historical order quantity, historical order generation quantity, historical pickup quantity, historical order category, historical income amount, historical target value, historical target value achievement, historical performance ranking, etc.

[0064] In operation S230, the i-th predicted target value of the i-th branch is generated according to the i-th target data to obtain M predicted target values.

[0065] According to an embodiment of the present disclosure, the i-th target data of the i-th business branch can be input into the neural network model, and the i-th predicted target value of the i-th business branch can be automatically output by using the neural network model. For example, the basic information data of the i-th business branch and the historical data generated by the i-th business branch within a preset time period can be input into a long short-term memory model, the basic information data of the i-th business branch and the historical data generated by the i-th business branch within the preset time period can be processed by using the long short-term memory model, the i-th predicted target value of the i-th business branch can be generated, and M predicted target values can be obtained.

[0066] In operation S240, the M predicted target values are optimized according to the reference target value and the predetermined constraint condition, and the optimized target value of each of the M business branches is displayed.

[0067] According to an embodiment of the present disclosure, the reference target value and the M predicted target values can be input into an optimization solving model, the M predicted target values can be optimized according to the reference target value and the predetermined constraint condition by using the optimization solving model, the optimized target value of each of the M business branches can be automatically output, and the output optimized target value can be displayed on the terminal device or the logistics system of each of the M business branches, or can be displayed on the terminal device or the management system of the target regional management department.

[0068] According to an embodiment of the present disclosure, the predetermined constraint condition can include a per capita working time, a number of employees, an employee turnover rate, an order volume, a delivery volume, a pickup volume, a pickup timeliness rate, a departure timeliness rate, a delivery timeliness rate, a loss rate, a service score, and the like.

[0069] According to an embodiment of the present disclosure, the target data including the basic information data and the historical data of the business branch is obtained from the database, the predicted target value of the business branch is generated according to the target data, the predicted target value is optimized according to the reference target value and the predetermined constraint condition, and the optimized target value of the business branch is displayed. By comprehensively considering the historical information and the basic information of the business branch, the target value of the business branch is predicted, and by further optimizing the target value according to the reference target value and the constraint condition, the accuracy and rationality of the recommended target value in the logistics system are improved, the internal relationship between the historical information and the basic information of the business branch and the target value of the business branch is mined, and at the same time, the target value is automatically generated, and the manual input is reduced.

[0070] According to an embodiment of the present disclosure, the above data processing method further includes: for the i-th business branch, obtaining target business branch information within a predetermined distance from the i-th business branch; and adjusting the i-th predicted target value according to the target business branch information.

[0071] According to an embodiment of the present disclosure, the predetermined distance can be a pickup range of the i-th business branch, or a fixed distance from the i-th business branch, for example, 1000 meters.

[0072] According to an embodiment of the present disclosure, the target branch can be a branch of a different platform from the i-th branch.

[0073] According to an embodiment of the present disclosure, before obtaining the target branch information, authorization or consent of the target branch is required. The target branch information can include the business scope of the target branch, the number of personnel, the number of target branches, and the like.

[0074] According to an embodiment of the present disclosure, the target branch information includes the number of target branches.

[0075] According to an embodiment of the present disclosure, adjusting the i-th predicted target value according to the target branch information includes: in the case that the number of target branches is greater than or equal to a predetermined threshold, adjusting the i-th predicted target value downward by a preset proportion.

[0076] According to an embodiment of the present disclosure, the number of target branches within a predetermined distance from the branch has an impact on the target value of the branch. When the number of target branches exceeds a predetermined threshold, adjusting the predicted target value by a predetermined proportion can make the generated predicted target value more accurate and reasonable.

[0077] According to an embodiment of the present disclosure, the above data processing method further includes: obtaining customer information of the i-th branch, the customer information including customer fluctuation information; and adjusting the i-th predicted target value according to the customer fluctuation information.

[0078] According to an embodiment of the present disclosure, the customer fluctuation can be an increase in the number of customers or a decrease in the number of customers.

[0079] According to an embodiment of the present disclosure, by considering the impact of customer fluctuation of the branch on the predicted target value, the accuracy and rationality of generating the predicted target value are further improved.

[0080] According to an embodiment of the present disclosure, the above data processing method further includes: in the case that it is determined that the i-th target data includes target value adjustment data, determining a target value adjustment frequency according to the target value adjustment data; determining a target value fluctuation rule of the i-th branch according to the target value adjustment frequency; and adjusting the i-th predicted target value according to the target value fluctuation rule.

[0081] According to an embodiment of the present disclosure, the target value adjustment data can include a reference target value adjustment frequency, a reference target value adjustment range, a difference between a reference target value and an actual target value, a target value adjustment process, and the like.

[0082] According to an embodiment of the present disclosure, the method further comprises: in a case where it is determined that the i-th target data comprises target value adjustment data, determining a target value adjustment frequency and an adjustment range according to the target value adjustment data; determining a target value fluctuation rule of the i-th branch office according to the target value adjustment frequency and the adjustment range; and adjusting the i-th predicted target value according to the target value fluctuation rule.

[0083] According to an embodiment of the present disclosure, the adjustment range can comprise an adjusted target value type and an adjusted numerical range, for example, can be that the pickup quantity is adjusted from 1000 to 900.

[0084] According to an embodiment of the present disclosure, the fluctuation rule of the target value can be a change rule of the target value over time.

[0085] According to an embodiment of the present disclosure, adjusting the predicted target value of the branch office according to the target value fluctuation rule of the branch office can make the generated predicted target value more reasonable and reduce the number of target value adjustments.

[0086] According to an embodiment of the present disclosure, the data processing method further comprises: for the i-th branch office of the M branch offices, in a case where it is determined that the target time period has marketing information for the i-th branch office, adjusting the i-th predicted target value according to the marketing information and a predetermined rule.

[0087] According to an embodiment of the present disclosure, the marketing information can comprise preferential information of the i-th branch office and marketing information of a cooperation platform.

[0088] According to an embodiment of the present disclosure, adjusting the predicted target value according to the preferential information of the branch office and the marketing information of the cooperation platform can further improve the accuracy and rationality of the predicted target value.

[0089] According to an embodiment of the present disclosure, the neural network model can be used to generate the respective predicted target value of each branch office.

[0090] According to an embodiment of the present disclosure, generating the i-th predicted target value of the i-th branch office according to the i-th target data to obtain M predicted target values can comprise: inputting the i-th target data into a pre-trained neural network model to output the i-th predicted target value of the i-th branch office.

[0091] According to an embodiment of the present disclosure, the i-th target data can be preprocessed before being input into the neural network model. The preprocessing can comprise data cleaning, data conversion, feature scaling, etc.

[0092] According to an embodiment of the present disclosure, the preprocessed data can be converted into a three-dimensional tensor. It can include the following steps: window size definition, determining the number of time steps for each sample window, also known as sequence length or window size, which determines how many historical time steps each sample contains; sample generation, creating samples from sequence data, for each sample, some time steps in the past will be used as input sequence, and the value of the next time step will be used as output; split input and output: according to how many time steps are used as input, the data can be split into input features and output labels, the input features are the first two dimensions of the three-dimensional tensor, and the output labels are the third dimension of the tensor; convert to three-dimensional tensor, a sample window can be represented as a two-dimensional matrix, where each row represents a time step and each column represents a feature. By adding a dimension, these are stacked to form a three-dimensional tensor. The final three-dimensional tensor can have the shape (number of samples, number of time steps, feature dimension).

[0093] Figure 3 A flowchart for obtaining M respective optimization target values of the M branches is schematically shown according to an embodiment of the present disclosure.

[0094] As shown in Figure 3 The method for obtaining M respective optimization target values of the M branches includes operations S310 and S320.

[0095] In operation S310, for a jth prediction target value in the M prediction target values, a jth optimization interval is determined according to the jth prediction target value and a predetermined optimization rule, to obtain M optimization intervals, where 1≤j≤M.

[0096] In operation S320, M respective optimization target values of the M branches are determined from the M optimization intervals, the reference target value, and a predetermined constraint condition.

[0097] According to an embodiment of the present disclosure, the predetermined optimization rule can be a numerical range limiting rule based on the prediction target value. The optimization interval can be a numerical range determined according to the predetermined optimization rule based on the prediction target value. For example, the optimization interval can be a numerical range with a certain percentage of up and down deviation based on the prediction target value. Specifically, when the prediction target value is 1000 pieces of pickup volume, and the predetermined optimization rule is 10% up and down floating, the optimization interval can be 900 pieces to 1100 pieces.

[0098] According to an embodiment of the present disclosure, the optimization interval determined based on the prediction target value and the predetermined optimization rule can further improve the accuracy and reasonableness of the optimization target value.

[0099] Figure 4 A flowchart for determining M respective optimization target values of the M branches is schematically shown according to another embodiment of the present disclosure.

[0100] As Figure 4 shown, the method of determining the optimization target values of the M branches respectively includes S410-S430.

[0101] In operation S410, a jth candidate interval satisfying a predetermined constraint condition is determined from a jth optimization interval, to obtain M candidate intervals.

[0102] In operation S420, for each of the M candidate intervals, a candidate value satisfying a target function is determined from the candidate interval, to obtain M candidate values, wherein the target function is to minimize the deviation between the sum of the M candidate values and a reference target value.

[0103] In operation S430, the M candidate values are determined as the optimization target values of the M branches respectively.

[0104] According to an embodiment of the present disclosure, in the case of M=1, the target function is to minimize the deviation between the candidate value and the reference target value. In the case of M≥2, the target function is to minimize the deviation between the sum of the M candidate values and the reference target value. Specifically, the target function can be defined with reference to formula (1).

[0105]

[0106] wherein x i is the candidate target value of the ith branch, x 参考 is the reference target value, and M is the number of branches.

[0107] According to an embodiment of the present disclosure, the predetermined constraint condition includes at least one of the following: average work hours, number of employees, employee turnover rate, order volume, and collection volume.

[0108] According to an embodiment of the present disclosure, the constraint condition of the average work hours can be set with reference to formula (2).

[0109] (∑(f i *x i )) / (∑(f i *y i ))≤avg_Work_hours_limit (2)

[0110] wherein f i is the number of employees of the ith branch, y i is the average work hours of the ith branch, and avg_work_hours_limit is the average work hours limit.

[0111] The constraint condition of the number of employees can be set with reference to formula (3).

[0112] x i ≤≤employee_limit, for i = 1 to M (3)

[0113] Wherein, the employee_limit is the employee quantity limit.

[0114] According to the embodiments of the present disclosure, the range of the optimization interval is further narrowed by the predetermined constraint condition, a candidate interval is obtained, and the target value is more reasonable and accurate; then the target function is solved, M candidate values are obtained, and the target value is more consistent with the expected value of the management personnel, that is, the reference target value.

[0115] According to the embodiments of the present disclosure, according to the actual business adjustment, there may be a case of changing the reference target value.

[0116] According to the embodiments of the present disclosure, the above data processing method further comprises: in response to the change operation of the reference target value, updating the target function according to the changed reference target value.

[0117] According to the embodiments of the present disclosure, for each candidate interval in the M candidate intervals, determining a candidate value satisfying the target function from the candidate interval, obtaining M candidate values comprises: for each candidate interval in the M candidate intervals, determining a candidate value satisfying the updated target function from the candidate interval, obtaining M candidate values.

[0118] According to the embodiments of the present disclosure, the change operation of the reference target value can be a change of the pickup quantity, a change of the sending quantity, etc.

[0119] According to the embodiments of the present disclosure, the change operation of the reference target value can also be a change of the employee quantity, etc.

[0120] According to the embodiments of the present disclosure, when the change operation of the reference target value involves the predetermined constraint condition, the predetermined constraint condition can also be updated according to the changed reference target value.

[0121] According to the embodiments of the present disclosure, the above method further comprises: in response to the change operation of the reference target value, updating the target function and the predetermined constraint condition according to the changed reference target value.

[0122] According to the embodiments of the present disclosure, determining the jth candidate interval satisfying the predetermined constraint condition from the jth optimization interval to obtain M candidate intervals comprises: determining the jth candidate interval satisfying the updated predetermined constraint condition from the jth optimization interval to obtain the updated M candidate intervals.

[0123] According to an embodiment of the present disclosure, the determining, for each of the M candidate intervals, a candidate value satisfying the target function from the candidate interval to obtain the M candidate values comprises: determining, for each of the updated M candidate intervals, a candidate value satisfying the updated target function from the updated candidate interval to obtain the M candidate values.

[0124] According to an embodiment of the present disclosure, the data processing method can re-optimize the target value according to the quick response of the changed reference target value, so that the target value adjustment work of the sales department is more convenient and accurate.

[0125] According to an embodiment of the present disclosure, the processing method further comprises: sending the respective optimized target value to the logistics system of the M sales departments respectively; receiving a target value adjustment request sent by the logistics system of the i-th sales department, wherein the target value adjustment request comprises a pre-adjustment value; and in the case that the pre-adjustment value meets a predetermined condition, sending the pre-adjustment value to the logistics system of the i-th sales department.

[0126] According to an embodiment of the present disclosure, after the optimized target value is generated, it can be sent to the person in charge of each sales department through the logistics system, and the person in charge of the sales department can confirm it, such as accepting the optimized target value. The target value can be automatically uploaded to the sales department business system and displayed on the page through one-key confirmation of the logistics system. If the optimized target value is not accepted, the person in charge of the sales department can manually input a target value considered reasonable by the person in charge of the sales department and upload it.

[0127] According to an embodiment of the present disclosure, the target value adjustment request can further comprise a basis for target value adjustment.

[0128] According to an embodiment of the present disclosure, the predetermined condition can be a condition related to the adjustment range. For example, the predetermined condition can be that the adjustment ratio of the pre-adjustment value compared with the optimized target value is not more than 10%. The predetermined condition can also be a reasonable explanation given by the sales department about the pre-adjustment value.

[0129] According to an embodiment of the present disclosure, the pre-adjustment value and the basis for target value adjustment can be collected; and the predicted target value is adjusted according to the pre-adjustment value and the basis for target value adjustment.

[0130] Figure 5 A flowchart of a data processing method according to another embodiment of the present disclosure is schematically shown.

[0131] As shown in Figure 5 The flow of the data processing method comprises S510-S560.

[0132] In operation S510, the relevant data of each sales department is supplemented.

[0133] According to an embodiment of the present disclosure, the relevant data can include branch office basic information data and historical data generated by the branch office in a preset time period, and can also include target branch office information within a predetermined distance from the branch office, target value adjustment data, customer fluctuation data of the branch office, and marketing information of the branch office, etc.

[0134] In operation S520, the historical target value data of each branch office is supplemented, and the deviation of the target value from the actual value is calculated.

[0135] According to an embodiment of the present disclosure, the overall deviation rate of the target value setting can also be calculated according to the deviation of the target value from the actual value. The deviation rate is the ratio of the number of branch offices with unreasonable target value settings to the number of branch offices, and the number of branch offices is the sum of the number of branch offices with unreasonable target value settings and the number of branch offices with reasonable target value settings.

[0136] In operation S530, the data of the branch office is processed.

[0137] According to an embodiment of the present disclosure, the data of the branch office can be subjected to data cleaning, data cleaning, data conversion, feature scaling, three-dimensional tensor conversion, etc.

[0138] In operation S540, a neural network model is used to generate a predicted target value according to the processed data of the branch office.

[0139] In operation S550, an optimization solver is used to optimize the predicted target value according to the reference target value and the predetermined constraint condition, and the optimized target value of each branch office is displayed.

[0140] In operation S560, the optimized target value is issued to the branch office.

[0141] According to an example of the present disclosure, the target prediction value can be generated by a prediction target value generation network.

[0142] According to an embodiment of the present disclosure, the above data processing method can also be evaluated by the manual time of the branch office target value setting by the manager. The manual time of the branch office target value setting by the manager can be required to be less than the upper limit of the working hours of the branch office. Specifically, the formula (4) and the formula (5) can be referred to.

[0143] A h ≤ θ (5)

[0144]

[0145] Wherein, A h is the manual time of the branch office target value setting by the manager, c i is the required manual time of the i-th branch office target value setting, M is the number of branch offices, and θ is the upper limit of the working hours of the branch office.

[0146] According to the embodiments of the present disclosure, by applying the above data processing method to the online system, the target value setting of more than 3,000 branches can save about 30% of the manual operation time of the management personnel, the target value deviation rate is stable below 15%, which is obviously lower than the original offline manual setting deviation rate of 30%, the accuracy rate of historical back testing is above 80%, and after full implementation, more than 3,000 branches can achieve online real-time monitoring and correction of target values, improve the accuracy and rationality of target value setting, reduce manual input, and ensure the enthusiasm of the branches.

[0147] According to the embodiments of the present disclosure, the training step of the prediction target value generation network can include operation S001 to operation S005.

[0148] In operation S001, the prediction target value generation network is initialized: the hyperparameters of the prediction target value generation network are set, such as the number of hidden units, the learning rate, the optimizer, etc., and the prediction target value generation network object is created.

[0149] In operation S002, forward propagation: for each input sample, forward propagation calculation is performed through the prediction target value generation network. First, the values of the input gate, the forget gate, and the output gate are calculated according to the input of the current time step and the hidden state of the previous time step. Then, according to these gate control information flow and update the cell state and the hidden state. Finally, the output value is calculated according to the hidden state. The calculation formulas of the input gate, the forget gate, the output gate, the cell state, and the hidden state can refer to formulas (6) to (10).

[0150] Input gate: i t = σ(W i *[h t-1 , x i ]+b i ) (6)

[0151] Forget gate: f t = σ(W f *[h t-1 , x i ]+b f ) (7)

[0152] Output gate: o t = σ(W o *[h t-1 , x i ]+b o ) (8)

[0153] Cell state: c t = f t *c t-1 +i t *tanh(w c*[h t-1 , x t ] + b c ) (9)

[0154] Hidden state: h t = o t * tanh(c t ) (10)

[0155] where σ denotes the sigmoid activation function and tanh denotes the hyperbolic tangent activation function.

[0156] In operation S003, a loss function is calculated: the predicted value of the prediction target value generation network is compared with the actual label, and the loss function is calculated. The loss function can be root mean square error, cross-entropy loss, etc.

[0157] In operation S004, back propagation: according to the loss function, the gradient is calculated using the back propagation algorithm, and the parameters of the prediction target value generation network are updated through gradient descent or other optimization algorithms. This will make the prediction target value generation network gradually adjust to reduce the prediction error.

[0158] In operation S005, repeat iteration: repeat operations S002 to S004 until a specified number of training iterations is reached or a stopping condition is reached, for example, the loss function can be converged.

[0159] Figure 6 A block diagram of a data processing apparatus according to an embodiment of the present disclosure is schematically shown.

[0160] As shown in Figure 6 , the data processing apparatus 600 comprises a first acquisition module 610, a second acquisition module 620, a generation module 630 and an optimization module 640.

[0161] The first acquisition module 610 is configured to, in response to receiving a target value determination request for M branches, acquire reference target values of a target area, wherein the target area comprises M branches, and M is a positive integer greater than or equal to 1.

[0162] The second acquisition module 620 is configured to, for the i-th branch of the M branches, acquire basic information data of the i-th branch and historical data generated by the i-th branch in a preset time period from a database to obtain i-th target data, wherein 1≤i≤M.

[0163] The generation module 630 generates the i-th prediction target value of the i-th branch according to the i-th target data to obtain M prediction target values.

[0164] The optimization module 640 is configured to optimize the M prediction target values according to the reference target values and predetermined constraint conditions, and display the optimization target values of the M branches respectively.

[0165] According to an embodiment of the present disclosure, the optimization module 640 comprises a first determining sub-module and a second determining sub-module. The first determining sub-module is configured to determine, for the jth prediction target value in the M prediction target values, the jth optimization interval according to the jth prediction target value and a predetermined optimization rule, to obtain M optimization intervals, where 1≤j≤M; and the second determining sub-module is configured to determine, according to the M optimization intervals, the reference target value and a predetermined constraint condition, the optimization target value of each of the M branch offices from the M optimization intervals.

[0166] According to an embodiment of the present disclosure, the second determining sub-module comprises a first determining unit, a second determining unit and a third determining unit. The first determining unit is configured to determine, from the jth optimization interval, the jth candidate interval satisfying the predetermined constraint condition, to obtain M candidate intervals; the second determining unit is configured to determine, for each of the M candidate intervals, the candidate value satisfying the target function from the candidate interval, to obtain M candidate values, where the target function is to minimize the deviation between the sum of the M candidate values and the reference target value; and the second determining unit is configured to determine the M candidate values as the optimization target values of the M branch offices.

[0167] According to an embodiment of the present disclosure, the data processing apparatus further comprises an updating module. The updating module is configured to update the target function according to the changed reference target value in response to a change operation on the reference target value.

[0168] According to an embodiment of the present disclosure, the second determining unit comprises a determining sub-unit. The determining sub-unit is configured to determine, for each of the M candidate intervals, the candidate value satisfying the updated target function from the candidate interval, to obtain M candidate values.

[0169] According to an embodiment of the present disclosure, the data processing apparatus further comprises a third obtaining module and a first adjusting module. The third obtaining module is configured to obtain, for the ith branch office, the target branch office information within a predetermined distance from the ith branch office; and the first adjusting module is configured to adjust the ith prediction target value according to the target branch office information.

[0170] According to an embodiment of the present disclosure, the adjusting module comprises a lowering sub-module. The lowering sub-module is configured to lower the ith prediction target value by a preset proportion in the case that the number of target branch offices is greater than or equal to a predetermined threshold.

[0171] According to an embodiment of the present disclosure, the data processing apparatus further comprises a first determining module, a second determining module and a second adjusting module. The first determining module is configured to determine a target value adjustment frequency according to the target value adjustment data when the target value adjustment data is included in the ith target data. The second determining module is configured to determine a target value fluctuation rule of the ith business department according to the target value adjustment frequency. The second adjusting module is configured to adjust the ith predicted target value according to the target value fluctuation rule.

[0172] According to an embodiment of the present disclosure, the data processing apparatus further comprises a first sending module, a receiving module and a second sending module. The first sending module is configured to send the respective optimized target value to the logistics system of the M business departments. The receiving module is configured to receive a target value adjustment request sent by the logistics system of the ith business department, wherein the target value adjustment request comprises a pre-adjustment value. The second sending module is configured to send the pre-adjustment value to the logistics system of the ith business department when the pre-adjustment value satisfies a predetermined condition.

[0173] Any one or more of the modules, sub-modules, units, sub-units according to the embodiments of the present disclosure, or at least part of the functions of any one or more of the modules, sub-modules, units, sub-units can be implemented in one module. Any one or more of the modules, sub-modules, units, sub-units according to the embodiments of the present disclosure can be split into multiple modules for implementation. Any one or more of the modules, sub-modules, units, sub-units according to the embodiments of the present disclosure can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on substrate, a system on package, an application specific integrated circuit (ASIC), or any other reasonable way of hardware or firmware through integration or packaging of circuits, or in any one of software, hardware and firmware or in an appropriate combination of any of them. Alternatively, one or more of the modules, sub-modules, units, sub-units according to the embodiments of the present disclosure can be at least partially implemented as computer program modules that can perform corresponding functions when executed.

[0174] For example, any of the first obtaining module 610, the second obtaining module 620, the generating module 630 and the optimizing module 640 can be combined in one module / unit / sub-unit for implementation, or any of the modules / units / sub-units can be split into multiple modules / units / sub-units. Alternatively, at least part of the functions of one or more of the modules / units / sub-units can be combined with at least part of the functions of other modules / units / sub-units, and implemented in one module / unit / sub-unit. According to an embodiment of the present disclosure, at least one of the first obtaining module 610, the second obtaining module 620, the generating module 630 and the optimizing module 640 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on board, a system on package, an application specific integrated circuit (ASIC), or any other reasonable manner of hardware or firmware that can be integrated or packaged with a circuit, or implemented in any one of software, hardware and firmware or in a proper combination of any of the above. Alternatively, at least one of the first obtaining module 610, the second obtaining module 620, the generating module 630 and the optimizing module 640 can be at least partially implemented as a computer program module that can perform corresponding functions when the computer program module is run.

[0175] It should be noted that the data processing system part in the embodiments of the present disclosure corresponds to the data processing method part in the embodiments of the present disclosure, and the description of the data processing system part is specifically referred to the data processing method part, which will not be repeated here.

[0176] Figure 7 A block diagram of an electronic device suitable for implementing the above-described method according to an embodiment of the present disclosure is schematically shown.

[0177] Figure 7 The electronic device shown is only an example, and should not bring any limitation to the functions and use range of the embodiments of the present disclosure.

[0178] As Figure 7As shown, the electronic device 700 according to embodiments of the present disclosure includes a processor 701 that can perform various appropriate actions and processes according to programs stored in a read only memory (ROM) 702 or loaded into a random access memory (RAM) 703 from a storage section 708. The processor 701 can include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor, and / or a related chipset and / or a dedicated microprocessor (e.g., an application specific integrated circuit (ASIC)), and so on. The processor 701 can also include an on-board memory for cache use. The processor 701 can include a single processing unit or multiple processing units for executing different actions of the method processes according to embodiments of the present disclosure.

[0179] In the RAM 703, various programs and data required for the operation of the electronic device 700 are stored. The processor 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. The processor 701 performs various operations of the method processes according to embodiments of the present disclosure by executing the programs in the ROM 702 and / or the RAM 703. Note that the programs described above can also be stored in one or more memories other than the ROM 702 and the RAM 703. The processor 701 can also perform various operations of the method processes according to embodiments of the present disclosure by executing the programs stored in the one or more memories.

[0180] According to embodiments of the present disclosure, the electronic device 700 can further include an input / output (I / O) interface 705 that is also connected to the bus 704. The system 700 can further include one or more of the following components connected to the input / output (I / O) interface 705: an input section 706 including a keyboard, a mouse, etc.; an output section 707 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, a modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the input / output (I / O) interface 705 as necessary. A removable medium 711 such as a magnetic disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 710 as necessary, so that a computer program read out therefrom is installed in the storage section 708 as necessary.

[0181] According to an embodiment of the present disclosure, the method flow according to the embodiments of the present disclosure can be implemented as a computer software program. For example, the embodiments of the present disclosure include a computer program product comprising a computer program carrying out the program codes for executing the method shown in the flow chart. In such embodiments, the computer program can be downloaded and installed from a network through the communication part 709, and / or installed from the detachable medium 711. When the computer program is executed by the processor 701, the above-mentioned functions defined in the system, device, apparatus, module, unit, etc. of the embodiments of the present disclosure are executed. According to an embodiment of the present disclosure, the system, device, apparatus, module, unit, etc. described above can be implemented by computer program modules.

[0182] The present disclosure also provides a computer readable storage medium, which can be included in the device / apparatus / system described in the above embodiments, or exist separately without being assembled into the device / apparatus / system. The above computer readable storage medium carries one or more programs, which when executed, implement the method according to the embodiments of the present disclosure.

[0183] According to an embodiment of the present disclosure, the computer readable storage medium can be a non-volatile computer readable storage medium. For example, it can include but not limited to portable computer diskette, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any appropriate combination thereof. In the present disclosure, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus or device.

[0184] For example, according to an embodiment of the present disclosure, the computer readable storage medium can include the ROM 702 and / or the RAM 703 described above and / or one or more memories other than the ROM 702 and the RAM 703.

[0185] The embodiments of the present disclosure also include a computer program product comprising a computer program containing program codes for executing the method provided by the embodiments of the present disclosure, which are used to make the electronic device implement the data processing method provided by the embodiments of the present disclosure when the computer program product is running on the electronic device.

[0186] When the computer program is executed by the processor 701, the above-mentioned functions defined in the system / apparatus of the embodiments of the present disclosure are executed. According to an embodiment of the present disclosure, the system, device, apparatus, module, unit, etc. described above can be implemented by computer program modules.

[0187] In one embodiment, the computer program can be embodied on a tangible memory device, such as a magnetic storage device, an optical storage device, etc. In another embodiment, the computer program can be transmitted in a signal over a network, distributed across networks, downloaded and installed, and / or installed from a removable memory media 711. The computer program comprising the program code can be transmitted using any suitable network medium, including, but not limited to wireless, wired, etc., or any suitable combination of the foregoing.

[0188] According to an embodiment of the disclosure, program code for execution by a computer program can be written in any combination of one or more programming languages, and specifically, can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include, but are not limited to, Java, C++, python, "C", or similar programming languages. Program code can execute entirely on a user's computing device, partly on a user's device, partly on a remote computing device, or entirely on a remote computing device or server. In the latter scenario, the remote computing device can be connected to the user's computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing device, such as through the Internet using an Internet Service Provider.

[0189] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the boxes may occur in an order different from that marked in the accompanying drawings. For example, two boxes shown in succession may actually be executed substantially in parallel, or they may sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, as well as the combination of boxes in the block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or may be implemented using a combination of dedicated hardware and computer instructions. It will be understood by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or coupled in various ways, and all such combinations and / or couplings fall within the scope of the present disclosure.

[0190] The embodiments of the present disclosure are described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be used in combination to advantage. The scope of the present disclosure is defined by the appended claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A data processing method, comprising: in response to receiving a target value determination request for M branches, obtaining a reference target value of a target region, wherein the target region comprises the M branches, and M is a positive integer greater than or equal to 1; for an i th branch of the M branches, obtaining basic information data of the i th branch and historical data generated by the i th branch in a preset time period from a database to obtain i th target data, wherein 1≤i≤M; generating an i th predicted target value of the i th branch according to the i th target data to obtain M predicted target values; and optimizing the M predicted target values according to the reference target value and a predetermined constraint condition to display an optimized target value of each of the M branches.

2. The method of claim 1, wherein, The optimizing the M predicted target values according to the reference target value and a predetermined constraint condition to display an optimized target value of each of the M branches comprises: for a j th predicted target value of the M predicted target values, determining a j th optimization interval according to the j th predicted target value and a predetermined optimization rule to obtain M optimization intervals, wherein 1≤j≤M; determining the optimized target value of each of the M branches from the M optimization intervals according to the M optimization intervals, the reference target value and the predetermined constraint condition.

3. The method of claim 2, wherein, The determining the optimized target value of each of the M branches from the M optimization intervals according to the M optimization intervals, the reference target value and the predetermined constraint condition comprises: determining a j th candidate interval from the j th optimization interval that satisfies the predetermined constraint condition to obtain M candidate intervals; for each of the M candidate intervals, determining a candidate value from the candidate interval that satisfies an objective function to obtain M candidate values, wherein the objective function is to minimize a deviation between a sum of the M candidate values and the reference target value; determining the M candidate values as the optimized target value of each of the M branches. 4.The method of claim 3, further comprising: in response to a change operation for the reference target value, updating the objective function according to a changed reference target value; The for each of the M candidate intervals, determining a candidate value from the candidate interval that satisfies an objective function to obtain M candidate values comprises: for each of the M candidate intervals, determining a candidate value from the candidate interval that satisfies an updated objective function to obtain M candidate values. 5.The method of claim 1, further comprising: for the i th branch, obtaining target branch information within a predetermined distance from the i th branch; adjusting the i th predicted target value according to the target branch information.

6. The method of claim 5, wherein, The target branch information comprises a number of target branches; The adjusting the i th predicted target value according to the target branch information comprises: in a case where the number of target branches is greater than or equal to a predetermined threshold, lowering the i th predicted target value by a preset proportion. 7.The method of claim 1, further comprising: in a case where it is determined that the ith target data includes target value adjustment data, determining a target value adjustment number according to the target value adjustment data; determining a target value fluctuation rule of the ith business department according to the target value adjustment number; adjusting the ith predicted target value according to the target value fluctuation rule.

8. The method of claim 1, further comprising: sending respective optimized target values to logistics systems of the M business departments, respectively; receiving a target value adjustment request sent by the logistics system of the ith business department, wherein the target value adjustment request includes a pre-adjustment value; in a case where the pre-adjustment value meets a predetermined condition, sending the pre-adjustment value to the logistics system of the ith business department.

9. A data processing apparatus, comprising: a first obtaining module configured to, in response to receiving a target value determination request for M business departments, obtain a reference target value of a target region, wherein the target region includes the M business departments, and M is a positive integer greater than or equal to 1; a second obtaining module configured to, for an ith business department of the M business departments, obtain basic information data of the ith business department and historical data generated by the ith business department in a preset time period from a database to obtain ith target data, wherein 1≤i≤M; a generating module configured to generate an ith predicted target value of the ith business department according to the ith target data to obtain M predicted target values; and an optimizing module configured to optimize the M predicted target values according to the reference target value and a predetermined constraint condition to display respective optimized target values of the M business departments.

10. An electronic device, comprising: one or more processors; a memory configured to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method of any one of claims 1 to 8.

11. A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to implement the method of any one of claims 1 to 8.

12. A computer program product comprising a computer program that, when executed by a processor, implements the method of any one of claims 1 to 8.

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