A method for evaluating production changes in commodity industries based on production heat

Abstract

A method for evaluating production changes in a commodity industry based on production heat belongs to the field of commodity trading technology. The method includes the following steps: step 1001, selecting evaluation indicators and synthesizing them into industry-level evaluation indicators; step 1002, adjusting the industry-level evaluation indicators to standardized time series respectively; step 1003, obtaining standardized sequences of industry-level evaluation indicators; step 1004, synthesizing production heat; step 1005, evaluating production changes in the commodity industry through indexed production heat. In this method for evaluating production changes in a commodity industry based on production heat, the start-up, price difference and inventory data of each product in the industry are synthesized into industry-level start-up, price difference and inventory data, and the error effects between each other are eliminated to obtain more independent start-up, price difference and inventory data. Finally, the production heat index of the industry is formed through weighted synthesis, which solves the problem that the current single form does not fully reflect the production changes in the industry.

Description

Technical Field

[0001] The invention discloses an evaluation method for production changes in a bulk commodity industry based on production heat, belonging to the technical field of bulk commodity trading. Background Art

[0002] As the world's largest commodity trading country, my country's supply changes in the commodity industry directly affect the market trend. In view of this, it is particularly urgent to build an indicator that can accurately reflect the production changes in the commodity industry. In the existing technology, the industry prosperity index can be used to quickly analyze and judge the development stability, risks and future development trends of the industry. The industry prosperity index can be used to measure the operating conditions of enterprises and obtain guidance for their production, operation and investment. Therefore, in the prior art, the prosperity index is generally used as an objective evaluation standard for the ups and downs of the economy or business cycle. Therefore, in the prior art, those skilled in the art are generally committed to studying how to obtain a more accurate industry prosperity index, such as: the technical solution disclosed in the Chinese invention patent with publication number CN107256254A and patent name "A method for obtaining an industry prosperity index, a storage device and a terminal"; the technical solution disclosed in the Chinese invention patent with publication number CN108287867 A and patent name "Industry prosperity index generation method, device, equipment and storage medium thereof"; and the technical solution disclosed in the Chinese invention patent with publication number CN110969313A and patent name "A method and system for predicting a prosperity index based on deep learning".

[0003] However, the applicant of this application found in actual implementation that the industry prosperity index calculated by the existing technology as an evaluation standard is not completely applicable to the commodity production industry, such as (1) the diffusion method. The production index in the PMI calculated with the diffusion method as the core can only reflect the rising and falling directions and turning points of the enterprise's production, but cannot well reflect the absolute changes in the enterprise's production, cannot judge the current industry production level in the historical cycle, and cannot judge the future changes in the enterprise's production. (2) Synthesis method. The ex-factory price index of industrial products formed with the synthesis method as the core can measure the changes in production from the perspective of the industry, but this indicator can only start from the single dimension of price, and cannot observe the changes in the industry's production heat from multiple indicators related to production.

[0004] Therefore, the industry prosperity indexes currently generated by various methods mostly focus on the overall operating status of the industry and future trend forecasts, and pay little attention to the production situation at the enterprise level and lack accuracy. Most importantly, the above indicators rarely focus on detailed considerations at the production level.

[0005] Therefore, designing a technical solution for production changes in the commodity industry to guide practitioners in optimizing resource allocation, adjusting investment strategies, etc. has become an urgent problem to be solved in this field. Summary of the invention

[0006] The technical problem to be solved by the present invention is: to overcome the shortcomings of the prior art, to provide a method for synthesizing the start-up, price difference and inventory data of each product in the industry into the start-up, price difference and inventory data of the industry, and to eliminate the influence of mutual errors to obtain more independent start-up, price difference and inventory data, and finally to form the production heat of the industry through weighted synthesis, thereby solving the problem of the incomplete reflection of industry production changes in a single form in the current evaluation method of production changes in the commodity industry based on production heat.

[0007] The technical solution adopted by the present invention to solve the technical problem is: an evaluation method for production changes in bulk commodity industries based on production heat, characterized in that it includes the following steps:

[0008] Step 1001, selecting evaluation indicators, obtaining N samples in the industry, and synthesizing the evaluation indicators into industry-level evaluation indicators;

[0009] Step 1002, adjusting the industry-level evaluation indicators to their respective standardized time series;

[0010] Step 1003, eliminating the errors caused by the mutual influence between the evaluation indicators, and obtaining a standardized sequence of industry-level evaluation indicators;

[0011] Step 1004, synthesizing production heat through a standardized sequence of evaluation indicators;

[0012] Step 1005, assessing the production changes of the bulk commodity industry by using the indexed production heat to assess the industry's prosperity.

[0013] Preferably, the evaluation indicators include production start-up, price difference, and inventory; production start-up is defined as the ratio of product output to production capacity; price difference is defined as the difference between the finished product price and the raw material price of the product; and inventory is defined as the number of products that have not yet been sold.

[0014] Preferably, step 1001 includes the following steps:

[0015] Step 1001-1, obtaining the monthly data of production start, price difference and inventory corresponding to the historical T months of N sample products in the industry: production start data A1, price difference data A2, inventory data A3;

[0016] Step 1001-2, extracting features from the operation start data A1, the price difference data A2, and the inventory data A3, to obtain the operation start data B1, the price difference data B2, and the inventory data B3 after feature extraction;

[0017] Step 1001-3, obtaining the monthly production and import data of all products in the industry for the past T months;

[0018] Step 1001-4, calculating the weight of each product when synthesizing industry-level evaluation indicators;

[0019] Step 1001-5, weighted synthesis of industry-level evaluation indicators: industry-level operation data C1, industry-level price difference data C2, industry-level inventory data C3.

[0020] Preferably, step 1002 includes the following steps:

[0021] Step 1002-1, calculate the average of industry-level evaluation indicators in the historical T months: industry-level operation data C1, industry-level price difference data C2, and industry-level inventory data C3;

[0022] Step 1002-2, calculate the industry-level evaluation indicators in the historical T months: the average monthly deviation absolute value of the industry-level start-up data C1, the industry-level price difference data C2, and the industry-level inventory data C3;

[0023] Step 1002-3, obtain the current industry-level evaluation indicators: standardized time series of industry-level start-up data C1, industry-level price difference data C2, and industry-level inventory data C3.

[0024] Preferably, step 1003 includes the following steps:

[0025] Step 1003-1, calculating the industry-level evaluation indicators: adjustment coefficients corresponding to the industry-level operation data C1, the industry-level price difference data C2, and the industry-level inventory data C3;

[0026] Step 1003-2, calculate the industry-level evaluation indicators: the adjusted intercepts corresponding to the industry-level operation data C1, the industry-level price difference data C2, and the industry-level inventory data C3;

[0027] Step 1003-1, calculate the industry-level evaluation indicators: the standardized sequences corresponding to the industry-level operation data C1, the industry-level price difference data C2, and the industry-level inventory data C3.

[0028] Preferably, in step 1004, the calculation formula for production heat is:

[0029]

[0030] Among them, SI1, SI2, and SI3 represent the standardized sequences of industry-level start-up C1, industry-level price difference C2, and industry-level inventory C3, respectively.

[0031] Preferably, the calculation formula of the standardized sequence is:

[0032] SI i =k i *(S i (t)+d i )

[0033] Among them, k i where i = 1, 2, 3, respectively representing the adjustment coefficients of industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3, S i In (t), i = 1, 2, and 3, respectively, represent the standardized time series of industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3, d i Where i=1, 2, 3, representing the adjusted intercepts of industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3, respectively.

[0034] Preferably, the calculation formula for the standardized time series is:

[0035]

[0036] Among them, t represents the time point corresponding to the data, SD i where i = 1, 2, and 3, respectively representing the average monthly deviation absolute values ​​of industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3;

[0037] The formula for calculating the absolute value of the average monthly deviation is:

[0038]

[0039] Where T represents the number of selected historical months. Where i = 1, 2, 3, representing the mean of industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3 respectively;

[0040] The formula for calculating the mean is:

[0041]

[0042] Among them, C i Here, i=1, 2, and 3, respectively representing industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3.

[0043] Preferably, the formula for synthesizing the price index into an industry-level evaluation index is:

[0044]

[0045] Where j represents a single product selected in an industry, n represents the total number of products selected in the industry, and B iis the start-up, price difference and inventory data after feature extraction, i = 1, 2, 3, representing start-up, price difference and inventory respectively; ω j represents weight, i=1, 2, 3, representing the weight of operation, price difference, and inventory respectively.

[0046] The weight calculation formula is:

[0047]

[0048] Among them, CL j (t) represents the output of the jth product in each time range; JKL j (t) represents the import volume of the jth product in each time frame.

[0049] Preferably, feature extraction is performed on the operation start data A1, the price difference data A2, and the inventory data A3 to obtain the operation start data B1, the price difference data B2, and the inventory data B3 after feature extraction.

[0050] The construction start data B1 and price difference data B2 are obtained through the following formula:

[0051]

[0052] The inventory data B3 is obtained by the following formula:

[0053]

[0054] Among them, i=1, 2, 3, respectively represent the data types, i.e., start-up, price difference, and inventory, T represents the number of selected historical months, t represents the corresponding time point of the data, t=0 represents the data is the current value, and t=T represents the data is the previous value T months before the current period. i (t) represents the historical monthly operation, price difference and inventory data.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] This method for evaluating production changes in commodity industries based on production heat expands on the existing idea of ​​creating a synthetic index, combining the start-up, price difference and inventory data of each product in the industry into the start-up, price difference and inventory data of the industry. On this basis, the mutual influence between the data is eliminated through data adjustment to obtain more independent start-up, price difference and inventory data, and finally the production heat of the industry is formed through equal weighted synthesis. This index can more accurately reflect the changing characteristics of the industry, solving the problem of incomplete reflection of industry production changes in a single form.

[0057] This production heat-based evaluation method for commodity industry production changes combines production start-up, price difference and inventory into a "production heat" indicator through a specific algorithm, providing the industry with an intuitive, comparable and quantifiable supply status evaluation system, effectively filling the current gap in "indicators that can accurately reflect industry supply changes". The production heat indicator can also accurately capture the regular changes in the industry development cycle based on the analysis results of historical data, which is of great significance in guiding practitioners in optimizing resource allocation and adjusting investment strategies.

[0058] This production heat evaluation method for the commodity industry production changes based on production heat can calculate the production heat index, which can not only accurately reflect the direction of change in the industry's production heat and reflect the turning point of the industry's production situation, but also show the changes in the current industry's production heat and historical production heat through a variety of comparison methods such as year-on-year and month-on-month comparisons, reflecting the trend of changes in the industry's supply side, and can help companies determine the direction of future production heat changes. This makes up for the shortcomings of the production index calculated by the diffusion method.

[0059] This method of evaluating the changes in the production of the commodity industry based on production heat has a more comprehensive description dimension. The production heat index calculated by this invention gets rid of the shortcomings of the existing production index calculated by the synthesis method based on a single price or output indicator, which shows a single dimension. It more comprehensively reflects the changes in industry production from the three dimensions of operation, price difference and inventory. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 Flowchart of the methodology for assessing production changes in commodity industries based on production heat. DETAILED DESCRIPTION

[0061] Figure 1 The best embodiment of the present invention is shown below in conjunction with the attached Figure 1 The present invention is further described.

[0062] like Figure 1 As shown, a method for evaluating production changes in a commodity industry based on production heat includes the following steps:

[0063] Step 1001, among n samples in the industry, by product type, the data of operation, price difference, and inventory are synthesized into industry-level operation, price difference, and inventory;

[0064] Based on the in-depth understanding of the industry and the verification of research logic, three indicators are selected to measure the production process of enterprises, which can more directly reflect the changes in the production behavior of enterprises, namely, start-up, price difference and inventory.

[0065] Among them, the start-up rate is defined as the ratio of product output to production capacity, which reflects the operating load of the enterprise. It is the most direct manifestation of the enterprise managers' adjustment of production operations and has the highest stability. The higher the start-up rate, the faster the enterprise produces.

[0066] The price difference is defined as the difference between the finished product price and the raw material price of a product, reflecting the profitability of the product. Corporate managers will adjust production behavior based on changes in the price difference. The stability of this indicator is relatively poor, but it has a strong leading nature. The higher the price difference, the more willing the company is to speed up production to obtain higher profits.

[0067] Inventory is defined as the number of products that have not been sold in the production, trade, import and export links. It reflects the capital utilization of the enterprise. The stability and leading nature of this indicator are between the price difference and the start-up. The higher the inventory, the more funds are occupied by the enterprise, and the more difficult it is to carry out faster production.

[0068] The obtained start-up, price difference and inventory data include not only monthly data but also daily or weekly data, so the data needs to be frequency-converted. For the start-up and price difference data, it is necessary to calculate the average value of all daily or weekly data obtained as the monthly start-up and price difference data. For the inventory data, it is necessary to select the last inventory data obtained in the month as the monthly inventory data.

[0069] Step 1001 specifically includes the following steps:

[0070] Step 1001-1, obtain the monthly data of production start-up, price difference and inventory corresponding to N sample products in the industry, define production start-up, price difference and inventory as A1, A2, A3 respectively, where 1 represents production start-up, 2 represents price difference, and 3 represents inventory. In subsequent formulas, the letter i is used to replace it for easy recording, that is, i=1, 2, 3.

[0071] Step 1001-2, extract the characteristics of the above sample products in terms of operation, price difference and inventory changes, and obtain the operation data B1, price difference data B2 and inventory data B3 after feature extraction. In specific implementation, the larger the operation and price difference data, the higher the production enthusiasm of the enterprise, while the larger the inventory data, the lower the production enthusiasm. Therefore, in the process of data feature extraction, the operation data B1 and price difference data B2 are obtained by the following formula:

[0072]

[0073] The inventory data B3 is obtained by the following formula:

[0074]

[0075] Among them, i=1, 2, 3, respectively represent the data types, i.e., start-up, price difference, and inventory, T represents the number of selected historical months, t represents the corresponding time point of the data, t=0 represents the data is the current value, and t=T represents the data is the previous value T months before the current period. i In (t), i=1, 2, 3, representing the data types, namely, operation, price difference, and inventory, respectively, which means the operation, price difference, and inventory data of the historical months.

[0076] Step 1001-3, obtaining the monthly production and import data of all products in the industry for the past T months;

[0077] Step 1001-4, calculate the weight of each product at the synthesis industry level start-up, price difference, and inventory. The calculation formula is:

[0078]

[0079] Among them, CL j (t) represents the output of the jth product in each time frame (month); JKL j (t) represents the import volume of the jth product in each time frame (month), t represents the time point corresponding to the data, j represents a single product selected in an industry, and n represents the total number of products selected in the industry.

[0080] Step 1001-5, weighted synthesis of industry-level production start-up, industry-level price difference, and industry-level inventory.

[0081] The industry-level operating C1, industry-level price difference C2, and industry-level inventory C3 are calculated by the formula:

[0082]

[0083] Where j represents a single product selected in an industry, n represents the total number of products selected in the industry, and B i is the start-up, price difference and inventory data after feature extraction, i = 1, 2, 3, representing start-up, price difference and inventory respectively; ω j Represents weight, i=1, 2, 3, representing the weight of start-up, price difference, and inventory respectively.

[0084] Step 1002, adjust industry-level operations, spreads, and inventories to standardized time series;

[0085] Step 1002 specifically includes the following steps:

[0086] Step 1002-1, calculate the average of industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3 in the historical T months, and the calculation formula is:

[0087]

[0088] Among them, T represents the number of historical months selected, t represents the time point corresponding to the data, and C i Here, i=1, 2, and 3, respectively representing industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3.

[0089] Step 1002-2, calculate the average monthly deviation absolute value of the industry-level operating C1, industry-level price difference C2, and industry-level inventory C3 in the historical T months, and the calculation formula is:

[0090]

[0091] Among them, T represents the number of historical months selected, t represents the time point corresponding to the data, and C i Where i=1, 2, 3, respectively represents industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3. Here, i=1, 2, and 3, respectively representing the means of industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3.

[0092] Step 1002-3, obtain the standardized time series of the current industry-level production start C1, industry-level price difference C2, and industry-level inventory C3, and the calculation formula is:

[0093]

[0094] Among them, t represents the time point corresponding to the data, C i Where i=1, 2, 3, respectively represents industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3. where i = 1, 2, and 3, respectively representing the mean of industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3, SD i Where i=1, 2, and 3, respectively represent the average monthly deviation absolute values ​​of industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3.

[0095] Step 1003, eliminating the errors caused by the mutual influence of the three factors of production start-up, price difference and inventory, and obtaining relatively independent standardized sequences of production start-up, price difference and inventory;

[0096] Step 1003 specifically includes the following steps:

[0097] Step 1003-1, calculating the adjustment coefficients corresponding to industry-level production, price difference, and inventory;

[0098] The calculation formula is:

[0099]

[0100] Among them, SD iWhere i=1, 2, and 3, respectively represent the average monthly deviation absolute values ​​of industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3.

[0101] Step 1003-2, calculate the adjusted intercept corresponding to industry-level production, price difference, and inventory;

[0102] The calculation formula is:

[0103]

[0104] Among them, C i Here, i=1, 2, and 3, respectively representing industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3.

[0105] Step 1003-3, calculate relatively independent standardized sequences of start-up, price difference, and inventory;

[0106] The calculation formula is:

[0107] SI i =k i *(S i (t)+d i )

[0108] Among them, k i where i = 1, 2, 3, respectively representing the adjustment coefficients of industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3, S i In (t), i = 1, 2, and 3, respectively, represent the standardized time series of industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3, d i Where i=1, 2, 3, representing the adjusted intercepts of industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3, respectively.

[0109] Step 1004, synthesizing production heat through start-up, price difference, and inventory standardization sequence;

[0110] The calculation formula of production heat RD is:

[0111]

[0112] Among them, SI1, SI2, and SI3 represent the standardized sequences of industry-level start-up C1, industry-level price difference C2, and industry-level inventory C3, respectively.

[0113] Step 1005, evaluating the production changes of the bulk commodity industry by using the production heat index to evaluate the industry prosperity;

[0114] Step 1005 includes the following steps:

[0115] Step 1005-1, indexing the production heat to convert it into a production heat index;

[0116] The conversion formula is: Production heat index = RD*0.1+100. After the above indexation processing, the production heat value is set to fluctuate up and down with 100 as the baseline to conform to industry practices and cognition.

[0117] Step 1005-2, setting the extreme value range of the production heat index.

[0118] After the production heat value is indexed, use this sequence value as a reference to review the historical data (more than 5 years is recommended) to check whether there are abnormal values ​​and find out the reasons. If there is a problem with the original data, correct the original data.

[0119] Define the MAX value (maximum value) and the MIN value (minimum value). Under normal circumstances, the confidence level of the production heat index is as high as 95% within the range of [90, 110]. Therefore, 90 and 110 are the dividing points between the overcooling and overheating intervals. At this time, MAX = 110, MIN = 90. Therefore, the highest value interval of the production heat index is set to ">110", and the lowest value interval of the production heat index is set to "<=90".

[0120] Step 1005-3, setting the evaluation interval of the production heat index.

[0121] Benchmarking PMI, set intervals. Since production heat is mainly used to show changes in industry production, and the production index in the manufacturing PMI is also an indicator used to reflect changes in manufacturing production, by comparing the changes in the two data and combining them with the actual market operation, we can further match whether the production situation is in line with the industry situation.

[0122] When the production heat index is around 95 and 105, the production index trend of PMI and the actual production behavior in the industry will be adjusted. Therefore, 95 and 105 in the production heat value are defined as the critical points of the three middle intervals.

[0123] Set the middle range. Match it with the actual industry production situation. Under normal circumstances, set it as follows:

[0124] They are ">105&<=110", ">95&<=105", and ">90&<=95" respectively.

[0125] The above interval critical values ​​can be adjusted appropriately to match the interval width when necessary.

[0126] Step 1005-4, evaluate the industry production changes by dividing the five intervals.

[0127] The corresponding relationship between the interval and the assessment of the production changes of the lighting industry is as follows:

[0128] ">110" is the overheating range, ">105&<=110" is the slightly hot range, ">95&<=105" is the normal range, ">90&<=95" is the slightly cold range, and "><=90" is the overcooling range.

[0129] When the production heat value is greater than 110, that is, in the overheating range, it means that most companies in the industry are very active in production, and the supply-demand contradiction in the industry has reached its peak;

[0130] When the production heat index is between 105 and 110, that is, in the hot range, it means that some companies in the industry are actively producing and there is a certain supply-demand contradiction in the industry;

[0131] When the production heat index is between 95 and 105, that is, in the normal range, it means that the enterprises in the industry can ensure relatively rational production behavior and can basically maintain a state of supply and demand balance;

[0132] When the production heat index is between 90 and 95, that is, in the cold range, it means that some companies in the industry have begun to reduce their operations, and there is a certain amount of oversupply pressure;

[0133] When the production heat index is less than 90, that is, it is in the overcooling range, it means that most companies in the industry have maintained lower production activities and oversupply pressure has reached its peak.

[0134] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change or modify it into an equivalent embodiment with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention still belongs to the protection scope of the technical solution of the present invention.

Claims

1. A method for evaluating production changes in a commodity industry based on production heat, characterized by: The steps include: Step 1001, selecting evaluation indicators, obtaining N samples in the industry, and synthesizing the evaluation indicators into industry-level evaluation indicators; Step 1002, adjusting the industry-level evaluation indicators to their respective standardized time series; Step 1003, eliminating the errors caused by the mutual influence between the evaluation indicators, and obtaining a standardized sequence of industry-level evaluation indicators; Step 1004, synthesizing production heat through a standardized sequence of evaluation indicators; Step 1005, assessing the production changes of the bulk commodity industry through the indexed production heat and the industry prosperity.

2. The method for evaluating production changes in bulk commodity industries based on production heat according to claim 1, characterized in that: Evaluation indicators include production start-up, price difference, and inventory; production start-up is defined as the ratio of product output to production capacity; price difference is defined as the difference between the price of finished products and the price of raw materials; inventory is defined as the number of products that have not yet been sold.

3. The method for evaluating production changes in bulk commodity industries based on production heat according to claim 2, characterized in that: Step 1001 includes the following steps: Step 1001-1, obtaining the monthly data of production start, price difference and inventory corresponding to the historical T months of N sample products in the industry: production start data A1, price difference data A2, inventory data A3; Step 1001-2, extracting features from the operation start data A1, the price difference data A2, and the inventory data A3, to obtain the operation start data B1, the price difference data B2, and the inventory data B3 after feature extraction; Step 1001-3, obtaining the monthly production and import data of all products in the industry for the past T months; Step 1001-4, calculating the weight of each product when synthesizing industry-level evaluation indicators; Step 1001-5, weighted synthesis of industry-level evaluation indicators: industry-level operation data C1, industry-level price difference data C2, industry-level inventory data C3.

4. The method for evaluating production changes in bulk commodity industries based on production heat according to claim 2, characterized in that: Step 1002 includes the following steps: Step 1002-1, calculate the average of industry-level evaluation indicators in the historical T months: industry-level operation data C1, industry-level price difference data C2, and industry-level inventory data C3; Step 1002-2, calculate the industry-level evaluation indicators in the historical T months: the average monthly deviation absolute value of the industry-level operation data C1, the industry-level price difference data C2, and the industry-level inventory data C3; Step 1002-3, obtain the current industry-level evaluation indicators: standardized time series of industry-level start-up data C1, industry-level price difference data C2, and industry-level inventory data C3.

5. The method for evaluating production changes in bulk commodity industries based on production heat according to claim 2, characterized in that: Step 1003 includes the following steps: Step 1003-1, calculating the industry-level evaluation indicators: adjustment coefficients corresponding to industry-level operation data C1, industry-level price difference data C2, and industry-level inventory data C3; Step 1003-2, calculate the industry-level evaluation indicators: the adjusted intercepts corresponding to the industry-level operation data C1, the industry-level price difference data C2, and the industry-level inventory data C3; Step 1003-1, calculate the industry-level evaluation indicators: the standardized sequences corresponding to the industry-level operation data C1, the industry-level price difference data C2, and the industry-level inventory data C3.

6. The method for evaluating production changes in bulk commodity industries based on production heat according to claim 5 is characterized in that: In step 1004, the calculation formula for production heat is: Among them, SI1, SI2, and SI3 represent the standardized sequences of industry-level start-up C1, industry-level price difference C2, and industry-level inventory C3, respectively.

7. The method for evaluating production changes in bulk commodity industries based on production heat according to claim 6, characterized in that: The calculation formula for the normalized sequence is: SI i =k i *(S i (t)+d i ) Among them, k i where i = 1, 2, 3, respectively representing the adjustment coefficients of industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3, S i In (t), i = 1, 2, and 3, respectively, represent the standardized time series of industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3, d i Where i=1, 2, 3, representing the adjusted intercepts of industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3, respectively.

8. The method for evaluating production changes in bulk commodity industries based on production heat according to claim 7, characterized in that: The calculation formula for the standardized time series is: Among them, t represents the time point corresponding to the data, SD i where i = 1, 2, and 3, respectively representing the average monthly deviation absolute values ​​of industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3; The formula for calculating the absolute value of the average monthly deviation is: Where T represents the number of selected historical months. where i = 1, 2, and 3, respectively representing the mean of industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3; C i (t) represents the time series of industry-level operations, spreads, and inventories, representing the corresponding values ​​within the monthly range; The formula for calculating the mean is: Among them, C i Here, i=1, 2, and 3, respectively representing industry-level production start-up C1, industry-level price difference C2, and industry-level inventory C3.

9. The method for evaluating production changes in bulk commodity industries based on production heat according to claim 3, characterized in that: The formula for synthesizing the price index into an industry-level evaluation index is: Where j represents a single product selected in an industry, n represents the total number of products selected in the industry, and B i is the start-up, price difference and inventory data after feature extraction, i = 1, 2, 3, representing start-up, price difference and inventory respectively; ω j represents weight, i=1, 2, 3, representing the weight of operation, price difference, and inventory respectively. The weight calculation formula is: Among them, CL j (t) represents the output of the jth product in each time range; JKL j (t) represents the import volume of the jth product in each time frame.

10. The method for evaluating production changes in bulk commodity industries based on production heat according to claim 3, characterized in that: Feature extraction is performed on the operation start data A1, price difference data A2, and inventory data A3 to obtain the operation start data B1, price difference data B2, and inventory data B3 after feature extraction. The construction start data B1 and price difference data B2 are obtained through the following formula: The inventory data B3 is obtained by the following formula: Among them, i = 1, 2, 3, respectively represent the data types, i.e., start-up, price difference, and inventory, T represents the number of selected historical months, t represents the corresponding time point of the data, t = 0 represents the data is the current value, t = T represents the data is the previous value T months before the current period, A i (t) represents the historical monthly operation, price difference and inventory data.

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