Logistics cost evaluation method and system

Abstract

The invention discloses a logistics cost evaluation method and system, and the method comprises the steps: calculating the actual values of at least two early warning evaluation indexes according to target logistics data; calculating a weight corresponding to the early warning evaluation index according to the target historical logistics data of the early warning evaluation index; the target historical logistics data is divided into a plurality of gears, the threshold value of each gear is calculated according to the target historical logistics data, the numerical value range of each gear is divided according to the threshold value, and each gear corresponds to a respective standard coefficient; according to the numerical range of each gear, determining a standard coefficient of the gear corresponding to the actual value; calculating a standard value of each gear according to the target historical logistics data; according to the actual value, the weight, the standard coefficient of the gear corresponding to the actual value and the standard value of the gear corresponding to the actual value, calculating the score of the early warning evaluation index; and calculating a total score of the target logistics data according to the score of each early warning evaluation index.

Description

Technical field:

[0001] The present invention relates to the field of logistics management, and in particular to a logistics cost evaluation method and system. Background technology:

[0002] With the rapid development of globalization and e-commerce, the logistics industry has become increasingly prominent in the global supply chain. As an important part of the operating costs of enterprises, logistics costs not only directly affect the profit margins of enterprises, but also are related to the efficiency and competitiveness of the supply chain.

[0003] Although the logistics industry has established a relatively complete system, it still lacks effective evaluation methods, which makes it difficult for companies to accurately assess risks and optimize costs, ultimately affecting logistics efficiency and cost control, and becoming a key factor restricting the further development of the industry. Summary of the invention:

[0004] In view of this, it is necessary to design a logistics cost evaluation method and system to improve the risk management capabilities and cost optimization level of the logistics industry.

[0005] In a first aspect, an embodiment of the present application provides a logistics cost evaluation method, comprising calculating an actual value of an early warning evaluation index based on target logistics data, wherein the early warning evaluation index includes at least two; calculating a weight corresponding to the early warning evaluation index based on target historical logistics data of the early warning evaluation index; dividing the target historical logistics data into multiple gears, calculating a threshold value of each gear based on the target historical logistics data, and dividing a numerical range of each gear based on the threshold value, wherein each gear corresponds to its own standard coefficient; determining a standard coefficient of a gear corresponding to the actual value based on the numerical range of each gear; calculating a standard value of each gear based on the target historical logistics data; calculating a score of the early warning evaluation index based on the actual value, the weight, the standard coefficient of the gear corresponding to the actual value, and the standard value of the gear corresponding to the actual value; and calculating a total score of the target logistics data based on the score of each early warning evaluation indicator.

[0006] In a second aspect, an embodiment of the present application provides a logistics cost evaluation system, characterized in that it includes: an actual value calculation module, which is used to calculate the actual value of the early warning evaluation index according to the target logistics data, wherein the early warning evaluation index includes at least two; a weight calculation module, which is used to calculate the weight corresponding to the early warning evaluation index according to the target historical logistics data of the early warning evaluation index; a standard coefficient calculation module, which is used to divide the target historical logistics data into multiple gears, calculate the threshold value of each gear according to the target historical logistics data, and divide the numerical range of each gear according to the threshold value, wherein each gear corresponds to its own standard coefficient; a standard coefficient determination module, which is used to determine the standard coefficient of the gear corresponding to the actual value according to the numerical range of each gear; a standard value calculation module, which is used to calculate the standard value of each gear according to the target historical logistics data; a score calculation module, which is used to calculate the score of the early warning evaluation index according to the actual value, the weight, the standard coefficient of the gear corresponding to the actual value, and the standard value of the gear corresponding to the actual value; a total score calculation module, which is used to calculate the total score of the target logistics data according to the score of each of the early warning evaluation indicators.

[0007] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a memory, a processor, and computer executable instructions stored in the memory and executable on the processor, wherein the computer executable instructions, when executed by the processor, implement the steps of the method for connecting to a base station as described in the first aspect above.

[0008] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which is used to store computer-executable instructions. When the computer-executable instructions are executed by a processor, the steps of the method for connecting to a base station as described in the first aspect above are implemented.

[0009] A logistics cost evaluation method and system provided in the embodiments of the present application calculates weights, standard coefficients and standard values ​​in a scientific and flexible manner, calculates the scores of various early warning evaluation indicators, and thus obtains the scores of target logistics costs. This not only improves the objectivity and accuracy of the evaluation results, but also meets the characteristics and requirements of specific industries, effectively filling the gap in logistics cost evaluation. Description of the drawings:

[0010] Attached Figure 1 It is a flow chart of a logistics cost evaluation method provided in an embodiment of the present application;

[0011] Attached Figure 2 It is a structural diagram of a logistics cost evaluation system provided in an embodiment of the present application;

[0012] Attached Figure 3It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. Specific implementation method:

[0013] The technical solutions in the embodiments of the present application will be clearly described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application are within the scope of protection of this application. The logistics cost evaluation method and system provided in the embodiments of the present application are described in detail below through specific embodiments and their application scenarios.

[0014] Figure 1 1 is a flow chart of a logistics cost evaluation method provided in an embodiment of the present application. The method 100 can be executed by a terminal device. In other words, the method can be executed by software or hardware installed in the terminal device. Figure 1 As shown, the method may include the following steps.

[0015] S110: Calculate the actual value of the early warning evaluation index according to the target logistics data.

[0016] Among them, the early warning evaluation indicators include at least two. Specifically, the target logistics data refers to the logistics data generated by an enterprise or organization for logistics activities in a specific period, for example, it can be the logistics data consumed by Y City in the process of logistics management and operation in 2017. The early warning evaluation indicators refer to a series of quantitative indicators used to monitor and evaluate the efficiency, cost, risk and performance of logistics activities in the process of logistics management and operation, for example, it can be the storage cost of a single box and the sorting cost of a single box, and the corresponding actual values ​​are the actual amount of the storage cost of a single box and the actual amount of the sorting cost of a single box.

[0017] In the embodiment of the present application, before step S110, it can also include obtaining target logistics data and early warning evaluation indicators, and the target logistics data and early warning evaluation indicators can be specified by the administrator.

[0018] S120: Calculate the weight corresponding to the early warning evaluation indicator according to the target historical logistics data of the early warning evaluation indicator.

[0019] In the embodiment of the present application, the target historical logistics data can be the actual value of the early warning evaluation index of each province / municipality in the industry during a certain period of time. For example, the early warning evaluation index is the storage fee per box, and the historical logistics data of the storage fee per box can be the actual amount of each province / municipality in the country from 2020 to 2021.

[0020] S130: Divide the target historical logistics data into multiple gears, calculate the threshold of each gear according to the target historical logistics data, and divide the value range of each gear according to the threshold.

[0021] Each level has its own standard coefficient. Specifically, the target historical logistics cost data can be divided into multiple levels, and the threshold of each level can be calculated by statistical methods. According to the threshold, the specific numerical range corresponding to each level can be determined. For example, the historical logistics cost data can be divided into five levels: "excellent", "good", "medium", "low" and "poor", and each level corresponds to a specific numerical range.

[0022] S140: Determine the standard coefficient of the gear corresponding to the actual value according to the numerical range of each gear.

[0023] In one possible implementation, dividing the target historical logistics data into multiple levels may include dividing the target historical logistics data into 5 levels, the 5 levels being excellent, good, medium, low and poor, and the corresponding standard coefficients being 1, 0.8, 0.6, 0.4 and 0.2, respectively.

[0024] For example, when the early warning evaluation indicator is the storage cost of a single box, it is determined within which numerical range the actual value of the storage cost of a single box is located, thereby determining the standard coefficient corresponding to the actual value of the storage cost of a single box.

[0025] S150: Calculate the standard value of each gear according to the historical logistics data.

[0026] Specifically, the SUMIF function may be used to calculate the standard value of each gear. In the embodiment of the present application, the standard value may be the average value of each gear range.

[0027] S160: Calculate the score of the early warning evaluation index according to the actual value, the weight, the standard coefficient of the gear corresponding to the actual value, and the standard value of the gear corresponding to the actual value.

[0028] When calculating the scores of early warning evaluation indicators, a systematic assessment can be conducted based on actual values, weights, standard coefficients and standard values. The specific calculation method can be determined based on actual conditions.

[0029] S170: Calculate the total score of the target logistics cost according to the scores of each early warning evaluation indicator.

[0030] After calculating the score of each early warning evaluation indicator, the score of each early warning evaluation indicator is added up to obtain the total score of the target logistics cost.

[0031] The logistics cost evaluation method provided in this application integrates the analysis of multiple early warning evaluation indicators, calculates the actual values ​​of various indicators, reasonably allocates weights, calculates standard coefficients and standard values, and finally integrates them into a comprehensive score. This innovative strategy significantly enhances the accuracy and reliability of the evaluation through flexible adjustment of the scoring mechanism, provides a scientific basis for corporate decision-making, helps companies understand subtle changes in logistics costs, can proactively identify potential risks, and promote cost control and efficiency improvement.

[0032] In a possible implementation, the early warning evaluation indicators may include single-box storage costs, single-box sorting costs, single-box distribution costs, single-box management costs, single-box transportation energy consumption costs, logistics cost rate, labor cost ratio, and single-box logistics costs excluding labor and depreciation. The eight early warning evaluation indicators are directional, practical, measurable, and collectible. The directional nature reflects the problem-oriented and goal-oriented nature of indicator setting. By evaluating and analyzing the indicator results, logistics management is improved to ensure that the logistics activities of each enterprise are consistent with their high-quality development goals. Practicality means that the indicators can objectively and truly reflect the development of the logistics work of the unit, and have application value and operability for evaluation and analysis. Measurability means that the indicators are quantifiable and comparable. Collectibility means that the data used for indicator calculation can be dynamically updated and obtained in real time, and the data can be automatically obtained from existing financial, logistics and other systems to the maximum extent possible, so as to minimize the intervention and influence of human factors and subjective intentions on the accuracy of the data.

[0033] In a possible implementation, step S110 may include: allocating target logistics data based on the activity-based costing method, and calculating the actual value of the early warning evaluation index.

[0034] Activity-Based Costing (ABC) is a cost accounting and management method that focuses on identifying and measuring various operations within an enterprise and allocating costs to products or services more accurately. In the embodiment of the present application, the logistics cost is defined as the consumption of logistics resources using the activity-based costing method. The resources consumed by logistics activities are first allocated to the operations, and then the operation costs are accurately allocated to the logistics cost objects. Compared with the traditional cost calculation method, the activity-based costing method adopts a more reasonable multi-standard and multi-step allocation method, that is, different resource drivers are used for different resources, and different operation drivers are used for different operation centers to allocate indirect costs, so that the sense of ownership of costs is stronger.

[0035] The following are the steps to use activity-based costing to evaluate the actual values ​​of early warning indicators.

[0036] Step 1: Establish warehousing operation center, sorting operation center, distribution operation center and management operation center, and determine the logistics cost indicators of each operation center, as shown in Table 1.

[0037] Table 1.

[0038]

[0039] Step 2: Divide the logistics cost indicators of each operation center into direct cost indicators and indirect cost indicators. Among them, the indirect cost indicators mainly include electricity costs, repair costs, commodity loss costs, heating costs and labor protection costs, as shown in Table 2.

[0040] Table 2.

[0041]

[0042] Step 3: Determine the operating factors of each indirect cost indicator, among which the operating factor of electricity fee is degree, the operating factor of repair fee is number of repairs, the operating factor of commodity loss fee is the amount of loss, the operating factor of heating fee is area, and the operating factor of labor protection fee is number of users.

[0043] Step 4: Calculate the allocation rate of each activity driver according to the activity driver allocation rate calculation formula. The activity driver allocation rate calculation formula is as follows:

[0044] Activity driver allocation rate = A / B

[0045] Among them, A is the total consumption of indirect cost indicators, and B is the total amount of operating factors corresponding to the indirect cost indicators. For example, when the indirect cost indicator is electricity charges, the operating factor allocation rate of electricity charges is as follows:

[0046] The operating factor allocation rate of electricity bill = total electricity consumption / total number of degrees

[0047] Step 5: According to the indirect cost calculation formula, the total consumption of indirect cost indicators is allocated to each operation center to obtain the indirect cost of each operation center during the statistical period. The calculation formula is as follows:

[0048] Indirect cost of each operation center = C × operation driver allocation rate

[0049] Among them, C is the amount of operating factors of each operating center corresponding to the indirect cost indicator. For example, when the indirect cost indicator is electricity cost, the electricity cost formula of the warehouse operation center is as follows:

[0050] The electricity cost of the storage operation center = the number of degrees consumed by the storage operation center × the allocation rate of the electricity cost to the operating cause

[0051] For example, Table 3 shows the electricity cost allocation results of each operation center when the target logistics data is the logistics cost of City Y in 2021.

[0052] Table 3.

[0053]

[0054] Only the allocation of electricity costs is shown here, and the allocation of other indirect cost drivers is performed in a similar manner.

[0055] Step 6: Gather the total amount of direct cost indicators of each operation center to obtain the direct cost of each operation center, add up the direct cost and indirect cost of each operation center to obtain the total logistics cost of each operation center.

[0056] Step 7: Based on the total logistics costs of each operation center, calculate the actual value of single-box warehousing costs, the actual value of single-box sorting costs, the actual value of single-box distribution costs, and the actual value of single-box management costs.

[0057] The actual value of the storage cost per box = total logistics cost of the storage operation center / total sales volume;

[0058] The actual value of the single-box sorting fee = total logistics cost of the sorting operation center / total sales volume;

[0059] The actual value of the single-box delivery cost = total logistics cost of the distribution operation center / total sales volume;

[0060] The actual value of the single-box management fee = total logistics cost of the management operation center / total sales volume.

[0061] Step 8: Calculate the actual value of the energy consumption cost of a single box of transportation, the actual value of the logistics cost rate, the actual value of the labor cost ratio, and the actual value of the logistics cost of a single box excluding labor and depreciation.

[0062] The actual value of the energy consumption cost of a single box of transportation = (fuel cost of the delivery vehicle + electricity cost of the delivery vehicle

[0063] + gas cost of delivery vehicles) / total sales;

[0064] The actual value of logistics cost rate = total logistics cost / sales revenue x 100%;

[0065] The actual value of the labor cost ratio = logistics labor cost / logistics cost;

[0066] The actual value of the logistics cost per box excluding labor and depreciation = (total logistics cost - logistics labor cost

[0067] -Depreciation of logistics fixed assets) / sales volume.

[0068] For example, Table 4 shows the actual values ​​of each early warning evaluation indicator of City Y from 2017 to 2021 calculated when the target logistics data is the logistics data of City Y from 2017 to 2021.

[0069] Table 4.

[0070]

[0071] The above-mentioned calculation of the actual value of each early warning evaluation indicator based on the activity-based cost method makes the cost ownership more stronger, avoids cost distortion, ensures the authenticity and reliability of cost information, and improves the efficiency and benefits of logistics operations.

[0072] In another possible implementation, step S110 may include: inputting the target logistics data into a pre-trained logistics cost calculation model to obtain the actual value of the early warning evaluation index.

[0073] In this possible implementation, before step S110, it may include obtaining target logistics data, using the target logistics data to train the BP neural network, and obtaining a logistics cost calculation model. Specifically, the BP neural network structure has three layers, namely, the input layer, the hidden layer, and the output layer. The independent variable input layer is the input layer; the hidden layer receives the original data from the input layer, and after complex function transformation and processing, transmits information to the output layer; the output layer receives the processed data of the hidden layer, and after corresponding function transformation processing, obtains the output data.

[0074] In the embodiment of the present application, the target logistics data can be the main parameters that affect the actual value of the early warning evaluation index. These parameters can be used as the input layer, and the output layer is the actual value of the early warning evaluation index. Specifically, each early warning evaluation index corresponds to a logistics cost prediction model. When the early warning evaluation index is the electricity fee, the parameters that affect the logistics warehousing electricity fee are equipment operation time, cargo handling volume, electricity price and overtime. The historical actual values ​​of these parameters and the historical actual values ​​of the electricity fee are used as training data to train and simulate the BP neural network, and the complex relationship between input and output is obtained, thereby establishing a logistics cost calculation model for electricity fees. After the logistics cost calculation model is established, the target logistics data, that is, the equipment operation time, cargo handling volume, electricity price and overtime in the forecast period are input into the model to obtain the actual value of the electricity fee. In practical applications, MATLAB software can be used to train the BP neural network.

[0075] This embodiment establishes a logistics cost calculation model through a BP neural network, aiming to obtain the actual value of the early warning evaluation index through the model, provide data support for the logistics cost budget, and then improve the level of logistics management, improve efficiency and reduce costs.

[0076] In one possible implementation, step S120 may include:

[0077] S1201: Standardize the target historical logistics data to obtain standardized target historical logistics data.

[0078] For the positive warning evaluation indicators, the following formula is used for standardization:

[0079]

[0080] For the reverse warning evaluation index, the following formula is used for standardization:

[0081]

[0082] For interval warning evaluation indicators, they should be converted into positive indicators and then dimensionless. ij ′=-|x ij -k| (where k is the appropriate value of the interval indicator) formula converts the interval indicator into a positive indicator, and then the converted data is applied to the formula used in the positive early warning evaluation indicator for dimensionless processing.

[0083] Among them, x ij is the actual value of the indicator, x ij ′ is the value after the interval indicator turns into a positive indicator, min(x ij ) is the minimum value of this indicator, max(x ij ) is the maximum value of this indicator, y ij is the dimensionless data.

[0084] S1202: Calculate the mean of the standardized target historical logistics data of each early warning evaluation indicator.

[0085] The calculation formula is as follows:

[0086]

[0087] in, is the mean of the standardized target historical logistics data, and n is the number of standardized target historical logistics data.

[0088] S1203: Calculate the standard deviation of the early warning evaluation index using the mean of the standardized target historical logistics data.

[0089] The calculation formula is as follows:

[0090]

[0091] Among them, σ is the standard deviation of the early warning evaluation index.

[0092] S1204: Use the standard deviation to calculate the weight of the early warning evaluation index.

[0093] The calculation formula is as follows:

[0094]

[0095] Among them, ω i is the weight of the early warning evaluation indicator.

[0096] For example, Table 5 shows the corresponding standard deviations and weights of each early warning evaluation indicator calculated when the target historical logistics data is the actual amount of each early warning evaluation indicator in each province / municipality during the statistical period.

[0097] Table 5.

[0098]

[0099] In another possible implementation, step S120 may include: inputting the target historical logistics data into a pre-trained weight calculation model to calculate the weight of the early warning evaluation index.

[0100] In this embodiment, before step S110, it also includes obtaining the target historical logistics data of each early warning evaluation index, using the target historical logistics data to perform model training, and obtaining a weight calculation model. Specifically, the process first standardizes or normalizes the acquired data, and then uses the target historical logistics data to construct a feature vector, and the target historical logistics data can be the historical actual value of the early warning evaluation index. For example, the 8 early warning evaluation indicators given in the embodiment of the present application are used to construct a feature vector using the historical actual values ​​within a specific time, and then the feature vector is used to train the neural network to obtain a weight calculation model. After the model is established, the target historical logistics data is input into the weight calculation model to obtain the weight of the early warning evaluation index. This embodiment can reduce human bias and make the weight distribution more objective by using a weight calculation model trained with a large amount of historical logistics data, while reducing the need for manual intervention and reducing labor costs.

[0101] In one possible implementation, step S150 may include: calculating the standard value of each gear using the SUMIF function based on the target historical logistics data. The target historical logistics data is the actual value of the early warning evaluation index within a specific time. In the embodiment provided in the present application, the standard value is the average of the actual values ​​of the early warning evaluation index in each gear.

[0102] For example, Table 6 shows the standard values ​​of each level calculated when the target historical logistics data is the actual amount of each early warning evaluation indicator in each province / municipality during the statistical period.

[0103] Table 6.

[0104]

[0105] In one possible implementation, step S160 may include the following steps.

[0106] S1601: Calculate the efficiency coefficient corresponding to the actual value according to the actual value, the current standard value and the upper standard value, wherein the current standard value is the standard value of the gear corresponding to the actual value, and the upper standard value is the standard value of the gear above the gear corresponding to the actual value.

[0107] The efficacy coefficient can be calculated according to the following formula.

[0108] Efficacy coefficient = (actual value - standard value of this level) / (standard value of upper level - standard value of this level)

[0109] S1602: Calculate the basis of the gear corresponding to the actual value according to the weight and the standard coefficient of the current gear, wherein the standard coefficient of the current gear is the standard coefficient of the gear corresponding to the actual value.

[0110] The standard coefficient can be calculated according to the following formula.

[0111] Current standard coefficient = weight × current standard coefficient × 100

[0112] S1603: Calculate the adjustment score of the actual value according to the efficacy coefficient, the basic score of the current level and the basic score of the upper level, wherein the basic score of the upper level is the basic score of the level above the level corresponding to the actual value.

[0113] This adjustment score can be calculated using the following formula.

[0114] Adjustment score = efficacy coefficient × (upper level basic score - current level basic score)

[0115] S1604: Calculate the score of the early warning evaluation index based on the basic score and adjustment score of this file.

[0116] The score of the early warning evaluation index = the basic score of this file + the adjustment score

[0117] In a possible implementation, after step S170, the following may be included: determining the early warning evaluation level according to the total score of the target logistics data, and outputting the risk status. Specifically, the early warning evaluation level can be pre-divided, and the division rules can be shown in the following table. Managers can understand the logistics status through the risk status and conduct logistics management and control, thereby improving the efficiency of logistics management and control and reducing logistics costs.

[0118] Table 7.

[0119]

[0120] For example, City Y used the above-mentioned logistics cost evaluation method to calculate the scores of each early warning evaluation indicator in 2021 and the total score of logistics costs in 2021, as shown in Table 8.

[0121] Table 8.

[0122]

[0123] Similarly, the total score of logistics data from other years can be calculated to identify risks and conduct logistics management and control.

[0124] The logistics cost evaluation method provided in the embodiment of the present application integrates the analysis of multiple early warning evaluation indicators, calculates the actual values ​​of various indicators, reasonably allocates weights, calculates standard coefficients and standard values, and finally integrates them into a comprehensive score. This innovative strategy significantly enhances the accuracy and reliability of the evaluation by flexibly adjusting the scoring mechanism, provides a scientific basis for corporate decision-making, helps companies understand subtle changes in logistics costs, can proactively identify potential risks, promote cost control and efficiency improvement, and effectively fills the gap in logistics cost evaluation.

[0125] Based on the logistics cost evaluation method described above, the present application embodiment discloses a logistics cost evaluation system 200, such as Figure 2 As shown, the system 200 mainly includes:

[0126] An actual value calculation module 210 is used to calculate the actual value of the early warning evaluation index according to the target logistics data, wherein the early warning evaluation index includes at least two;

[0127] A weight calculation module 220, for calculating the weight corresponding to the early warning evaluation indicator according to the target historical logistics data of the early warning evaluation indicator;

[0128] A standard coefficient calculation module 230 is used to divide the target historical logistics data into multiple gears, calculate the threshold of each gear according to the target historical logistics data, and divide the value range of each gear according to the threshold, wherein each gear corresponds to its own standard coefficient;

[0129] A standard coefficient determination module 240, for determining a standard coefficient of a gear corresponding to the actual value according to a numerical range of each gear;

[0130] A standard value calculation module 250, used to calculate the standard value of each gear according to the target historical logistics data;

[0131] A score calculation module 260, configured to calculate a score of the early warning evaluation index according to the actual value, the weight, the standard coefficient of the gear corresponding to the actual value, and the standard value of the gear corresponding to the actual value;

[0132] The total score calculation module 270 is used to calculate the total score of the target logistics data according to the scores of each of the early warning evaluation indicators.

[0133] In one possible implementation, the early warning evaluation indicators include single-box storage costs, single-box sorting costs, single-box distribution costs, single-box management costs, single-box transportation energy consumption costs, logistics cost rate, labor cost ratio, and single-box logistics costs excluding labor and depreciation.

[0134] In a possible implementation, the actual value calculation module 210 is further used to allocate the target logistics data based on the activity-based cost method to calculate the actual value of the early warning evaluation index.

[0135] In another possible implementation, the actual value calculation module 210 also inputs the target logistics data into a pre-trained logistics cost calculation model to obtain the actual value of the early warning evaluation index, and the target logistics data is the main parameter that affects the actual value of the early warning evaluation index.

[0136] In a possible implementation, the weight calculation module 220 is also used to standardize the target historical logistics data to obtain standardized target historical logistics data; calculate the mean of the standardized target historical logistics data for each of the early warning evaluation indicators; use the mean of the standardized target historical logistics data to calculate the standard deviation of the early warning evaluation indicator; and use the standard deviation to calculate the weight of the early warning evaluation indicator.

[0137] In another possible implementation, the weight calculation module 220 is further used to input the target historical logistics data into a pre-trained weight calculation model to calculate the weight of the early warning evaluation index.

[0138] In a possible implementation, the standard coefficient determination module 240 is also used to divide the target historical logistics data into five levels, wherein the five levels are excellent, good, medium, low and poor, and the corresponding standard coefficients are 1, 0.8, 0.6, 0.4 and 0.2 respectively.

[0139] In a possible implementation, the standard value calculation module 250 is further configured to calculate the standard value of each gear using a SUM IF function based on the target historical logistics data.

[0140] In a possible implementation, the score calculation module 260 is also used to calculate the efficacy coefficient corresponding to the actual value based on the actual value, the current gear standard value and the upper gear standard value, wherein the current gear standard value is the standard value of the gear corresponding to the actual value, and the upper gear standard value is the standard value of the upper gear of the gear corresponding to the actual value; calculate the basic score of the gear corresponding to the actual value based on the weight and the current gear standard coefficient, wherein the current gear standard coefficient is the standard coefficient of the gear corresponding to the actual value; calculate the adjustment score of the actual value based on the efficacy coefficient, the current gear basic score and the upper gear basic score, wherein the upper gear basic score is the basic score of the upper gear of the gear corresponding to the actual value; calculate the score of the early warning evaluation indicator based on the current gear basic score and the adjustment score.

[0141] Optional, such as Figure 3 As shown, an embodiment of the present application also provides an electronic device 300, including a processor 301, a memory 302, and a program or instruction stored in the memory 302 and executable on the processor 301. When the program or instruction is executed by the processor 301, each process of the above-mentioned logistics cost evaluation method is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0142] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned test state detection method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0143] The processor 301 is the processor in the electronic device 300 described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0144] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface and the processor are coupled, and the processor is used to run network-side device programs or instructions to implement the various processes of the embodiment of the above-mentioned logistics cost evaluation method, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0145] An embodiment of the present application also provides a computer program product, which includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor. When the program or instruction is executed by the processor, each process of the above-mentioned logistics cost evaluation method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0146] The above embodiments of the present application focus on describing the differences between the various embodiments. As long as the different optimization features between the various embodiments are not contradictory, they can be combined to form a better embodiment. Considering the simplicity of the text, they will not be repeated here.

[0147] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A logistics cost evaluation method, characterized in that: include: Calculate the actual value of the early warning evaluation index according to the target logistics data, wherein the early warning evaluation index includes at least two; Calculate the weight corresponding to the early warning evaluation indicator according to the target historical logistics data of the early warning evaluation indicator; Dividing the target historical logistics data into multiple gears, calculating the threshold of each gear according to the target historical logistics data, and dividing the value range of each gear according to the threshold, wherein each gear corresponds to its own standard coefficient; Determining the standard coefficient of the gear corresponding to the actual value according to the numerical range of each gear; Calculating the standard value of each gear according to the target historical logistics data; Calculate the score of the early warning evaluation index according to the actual value, the weight, the standard coefficient of the gear corresponding to the actual value, and the standard value of the gear corresponding to the actual value; According to the scores of each of the early warning evaluation indicators, the total score of the target logistics data is calculated.

2. The logistics cost evaluation method according to claim 1, characterized in that: The early warning evaluation indicators include single-box warehousing costs, single-box sorting costs, single-box distribution costs, single-box management costs, single-box transportation energy consumption costs, logistics cost rate, labor cost ratio, and single-box logistics costs excluding labor and depreciation.

3. The logistics cost evaluation method according to claim 1 or 2, characterized in that: The actual value of the early warning evaluation index is calculated according to the target logistics data, including: The target logistics data is allocated based on the activity-based costing method, and the actual value of the early warning evaluation index is calculated.

4. The logistics cost evaluation method according to claim 1 or 2, characterized in that: The actual value of the early warning evaluation index is calculated according to the target logistics data, including: The target logistics data is input into a pre-trained logistics cost calculation model to obtain the actual value of the early warning evaluation index. The target logistics data is the main parameter that affects the actual value of the early warning evaluation index.

5. The logistics cost evaluation method according to claim 1 or 2, characterized in that: Calculating the weight corresponding to the early warning evaluation indicator according to the target historical logistics data of the early warning evaluation indicator includes: Performing standardization processing on the target historical logistics data to obtain standardized target historical logistics data; Calculating the mean of the standardized target historical logistics data of each of the early warning evaluation indicators; Using the mean of the standardized target historical logistics data, calculating the standard deviation of the early warning evaluation indicator; The standard deviation is used to calculate the weight of the early warning evaluation index.

6. The logistics cost evaluation method according to claim 1 or 2, characterized in that: Calculating the weight corresponding to the early warning evaluation indicator according to the target historical logistics data of the early warning evaluation indicator includes: The target historical logistics data is input into a pre-trained weight calculation model to calculate the weight of the early warning evaluation index.

7. The logistics cost evaluation method according to claim 1 or 2, characterized in that: The dividing the target historical logistics data into a plurality of gears comprises: The target historical logistics data is divided into five levels, namely excellent, good, medium, low and poor, and the corresponding standard coefficients are 1, 0.8, 0.6, 0.4 and 0.2 respectively.

8. The logistics cost evaluation method according to claim 1, 2 or 7, characterized in that: Calculating the standard value of each gear according to the target historical logistics data includes: According to the target historical logistics data, the standard value of each gear is calculated using the SUMIF function.

9. The logistics cost evaluation method according to claim 1 or 2, characterized in that: Calculating the score of the early warning evaluation index according to the actual value, the weight, the standard coefficient of the gear corresponding to the actual value, and the standard value of the gear corresponding to the actual value includes: Calculate the power coefficient corresponding to the actual value according to the actual value, the current standard value and the upper standard value, wherein the current standard value is the standard value of the gear corresponding to the actual value, and the upper standard value is the standard value of the gear above the gear corresponding to the actual value; Calculate the basic score of the gear corresponding to the actual value according to the weight and the standard coefficient of the current gear, wherein the standard coefficient of the current gear is the standard coefficient of the gear corresponding to the actual value; Calculate the adjustment score of the actual value according to the efficacy coefficient, the basic score of the current gear and the basic score of the upper gear, wherein the basic score of the upper gear is the basic score of the gear above the gear corresponding to the actual value; The score of the early warning evaluation index is calculated based on the basic score of this file and the adjustment score.

10. A logistics cost evaluation system, characterized in that: include: An actual value calculation module, used to calculate the actual value of the early warning evaluation index according to the target logistics data, wherein the early warning evaluation index includes at least two; A weight calculation module, used to calculate the weight corresponding to the early warning evaluation indicator according to the target historical logistics data of the early warning evaluation indicator; A standard coefficient calculation module, used for dividing the target historical logistics data into multiple gears, calculating the threshold of each gear according to the target historical logistics data, and dividing the value range of each gear according to the threshold, wherein each gear corresponds to its own standard coefficient; A standard coefficient determination module, used to determine the standard coefficient of the gear corresponding to the actual value according to the numerical range of each gear; A standard value calculation module, used for calculating the standard value of each gear according to the target historical logistics data; A score calculation module, used to calculate the score of the early warning evaluation index according to the actual value, the weight, the standard coefficient of the gear corresponding to the actual value, and the standard value of the gear corresponding to the actual value; The total score calculation module is used to calculate the total score of the target logistics data according to the scores of each of the early warning evaluation indicators.

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