Zero-carbon park zone investment assessment method, device, storage medium and computer program product

Abstract

The application discloses a zero-carbon park area investment evaluation method and device, a storage medium and a computer program product, relates to the technical field of carbon emission management, and the method comprises the following steps: collecting enterprise-side energy data and park-side energy data; establishing an enterprise load curve according to the enterprise-side energy data and calculating the total carbon emission of the enterprise, and establishing a park green electricity curve according to the park-side energy data; calculating the time sequence matching degree of the enterprise load curve and the park green electricity curve; and generating an investment access list based on the time sequence matching degree and the total carbon emission of the enterprise. By calculating the time sequence matching degree of the park side and the enterprise side, the time sequence characteristics of the park green electricity output are used for park investment evaluation, and carbon emission is reduced.

Description

Technical Field

[0001] This application relates to the field of carbon emission management technology, and in particular to zero-carbon industrial park investment evaluation methods, equipment, storage media and computer program products. Background Technology

[0002] Currently, smart site selection for industries involved in zero-carbon industrial parks primarily serves the site selection needs of enterprises themselves, focusing on market demand and competition without considering the carbon budget constraints and zero-carbon park construction standards of the park itself. Furthermore, there is a lack of verification methods for the carbon emission data reported by enterprises during the investment attraction phase. The output of green electricity (solar and wind power) in the park fluctuates, and traditional assessments do not consider whether the electricity consumption characteristics of enterprises match the green electricity curve, resulting in the green electricity consumption ratio failing to meet standards after introduction. Carbon budget management cannot be dynamically adjusted according to the project introduction sequence, easily leading to budget overruns or idle resources.

[0003] Therefore, how to conduct investment promotion assessments based on the timing characteristics of green electricity output in the park and reduce carbon emissions in the park has become a technical problem that this application urgently needs to solve. Summary of the Invention

[0004] The main purpose of this application is to provide a zero-carbon industrial park investment evaluation method, equipment, storage medium and computer program product, which aims to solve the technical problem of how to evaluate industrial park investment based on the time-series characteristics of green electricity output and reduce carbon emissions of the industrial park.

[0005] To achieve the above objectives, this application proposes a method for evaluating investment attraction in zero-carbon industrial parks, the method comprising: Collect energy data from both the enterprise and industrial park sides; Establish enterprise load curves and calculate total carbon emissions based on enterprise-side energy data; establish park green electricity curves based on park-side energy data. Calculate the time-series matching degree between the enterprise load curve and the park green electricity curve; A list of companies eligible for investment is generated based on the time-series matching degree and the total carbon emissions of the enterprises.

[0006] In one embodiment, the step of collecting enterprise-side energy data and park-side energy data further includes: Compare the planned annual electricity consumption in the enterprise-side energy data with the remaining power supply capacity in the park-side energy data; If the planned annual electricity consumption is not greater than the remaining power supply capacity, then the following steps are performed: establish an enterprise load curve based on the enterprise-side energy data and estimate the total carbon emissions of the enterprise; establish a park green electricity curve based on the park-side energy data.

[0007] In one embodiment, the step of establishing an enterprise load curve and calculating the enterprise's total carbon emissions based on the enterprise-side energy data includes: Verify that historical load data exists in the enterprise-side energy data; If the historical load data exists in the enterprise-side energy data, then the historical load data is normalized to obtain a normalized load curve. The normalized load curve is fused with a preset industry load template using an exponentially weighted moving average method to obtain the enterprise load curve; If the historical load data is not found in the enterprise-side energy data, the enterprise industry code is determined from the enterprise-side energy data. The industry load template is retrieved based on the enterprise industry code, a confidence label is set for the industry load template, and the industry load template with the confidence label is used as the enterprise load curve. The planned annual electricity consumption is extracted from the enterprise-side energy data, and the baseline carbon intensity of the park is extracted from the park-side energy data. The total carbon emissions of the enterprise are obtained by multiplying the planned annual electricity consumption by the baseline carbon intensity of the park.

[0008] In one embodiment, the step of establishing a green electricity curve for the park based on the park-side energy data includes: The type of green power facility is determined based on the energy data from the park, and a typical time period is selected based on the type of green power facility. A green electricity curve for the park is established with the typical time period as the horizontal axis.

[0009] In one embodiment, the step of calculating the time-series matching degree of the enterprise load curve and the park green electricity curve includes: Calculate the dynamic time-normalized distance between the enterprise load curve and the park green electricity curve; The time-series matching degree is obtained by weighting the dynamic time warping distance using a preset time period weighting factor.

[0010] In one embodiment, the step of generating an investment attraction access list based on the time-series matching degree and the total carbon emissions of the enterprises includes: Extract the planned annual output value from the enterprise's energy data, and divide the enterprise's total carbon emissions by the planned annual output value to obtain the carbon emissions per unit output value. A two-dimensional evaluation coordinate system is established with the carbon emissions per unit output value as the horizontal axis and the time-series matching degree as the vertical axis. Based on the two-dimensional evaluation coordinate system, candidate investment projects are selected, and the time-series analysis of the candidate investment projects is performed to obtain an investment access list.

[0011] In one embodiment, the step of selecting candidate investment projects based on the two-dimensional evaluation coordinate system and performing time-series analysis on the candidate investment projects to obtain an investment access list includes: The projects to be evaluated in the first quadrant of the two-dimensional evaluation coordinate system are included in the candidate investment projects; Set a set of constraints for the candidate investment projects, and perform multiple rounds of rolling time-series simulation on the candidate investment projects based on the set of constraints to obtain the time-series simulation path; The investment access list is generated based on the time-series deduction path and the candidate investment projects.

[0012] In addition, to achieve the above objectives, this application also proposes a zero-carbon industrial park investment promotion evaluation device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the zero-carbon industrial park investment promotion evaluation method as described above.

[0013] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the zero-carbon industrial park investment evaluation method described above.

[0014] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the zero-carbon industrial park investment evaluation method described above.

[0015] One or more technical solutions proposed in this application have at least the following technical effects: First, by collecting energy data from both the enterprise and industrial park sides, enterprise load curves and industrial park green electricity curves are established, transforming the energy consumption behavior of enterprises and industrial parks into quantifiable and comparable time series. Then, by calculating the time series matching degree of the two curves, the green electricity consumption capacity of enterprises in the time dimension is accurately measured numerically. Furthermore, an investment access list is generated based on the time series matching degree and the total carbon emissions of enterprises, ensuring that the introduced projects have low emissions in terms of carbon accounting and are in sync with the green electricity supply in terms of physical operation. This enables the assessment of industrial park investment based on the time series characteristics of the green electricity output of the industrial park, thereby reducing carbon emissions. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the first embodiment of the zero-carbon industrial park investment evaluation method of this application; Figure 2 This is a flowchart illustrating the second embodiment of the zero-carbon industrial park investment evaluation method in this application. Figure 3 This is a flowchart illustrating the fourth embodiment of the zero-carbon industrial park investment evaluation method in this application. Figure 4 This is a schematic diagram of the module structure of the zero-carbon industrial park investment promotion evaluation device according to an embodiment of this application; Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the zero-carbon park investment promotion evaluation method in this application embodiment.

[0019] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0021] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0022] The main solution of this application embodiment is: collecting enterprise-side energy data and park-side energy data; establishing an enterprise load curve and calculating the total carbon emissions of the enterprise based on the enterprise-side energy data, and establishing a park green electricity curve based on the park-side energy data; calculating the time-series matching degree of the enterprise load curve and the park green electricity curve; and generating an investment attraction access list based on the time-series matching degree and the total carbon emissions of the enterprise.

[0023] In this embodiment, for ease of description, the zero-carbon industrial park investment evaluation system will be used as the implementing entity for the following description.

[0024] This application's embodiments take into account the following: Currently, intelligent site selection for industries involved in zero-carbon parks primarily serves the site selection needs of enterprises themselves, focusing on market demand and competition without considering the carbon budget constraints and zero-carbon park construction standards on the park side. Furthermore, there is a lack of verification methods for the carbon emission data self-reported by enterprises during the investment promotion phase. The output of green electricity (photovoltaic and wind power) in the park fluctuates, and traditional assessments do not consider whether the electricity consumption characteristics of enterprises match the green electricity curve, resulting in the green electricity consumption ratio failing to meet standards after introduction. Carbon budget management cannot be dynamically adjusted according to the project introduction sequence, easily leading to budget overruns or idle resources.

[0025] Therefore, this application provides a solution. First, by collecting energy data from both the enterprise and industrial park sides, enterprise load curves and industrial park green electricity curves are established, transforming the energy consumption behavior of enterprises and industrial parks into quantifiable and comparable time series. Then, by calculating the time series matching degree of the two curves, the green electricity consumption capacity of enterprises in the time dimension is accurately measured numerically. Furthermore, an investment attraction access list is generated based on the time series matching degree and the total carbon emissions of enterprises, ensuring that the introduced projects have low emissions in terms of carbon accounting and are in sync with the green electricity supply in terms of physical operation. This enables the assessment of industrial park investment attraction based on the time series characteristics of the green electricity output of the industrial park, thereby reducing carbon emissions.

[0026] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a zero-carbon industrial park investment promotion evaluation system. The following description uses a zero-carbon industrial park investment promotion evaluation system as an example to illustrate this embodiment and the subsequent embodiments.

[0027] Based on this, the embodiments of this application provide a method for evaluating investment attraction in zero-carbon industrial parks, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the zero-carbon industrial park investment evaluation method of this application.

[0028] In this embodiment, the zero-carbon industrial park investment evaluation method includes steps S10 to S40: Step S10: Collect energy data from the enterprise side and energy data from the park side; It should be noted that, in this embodiment, enterprise-side energy data refers to a set of information directly related to energy consumption filled in or provided by prospective resident enterprises, including planned annual electricity consumption, planned annual output value, industry code, and optional historical load data.

[0029] Among them, the planned annual electricity consumption is the enterprise's estimate of the total electricity consumption in the coming year, and it is the basic parameter for carbon emission estimation and capacity verification; the industry code is used to identify the national economic industry classification to which the enterprise belongs, and it is the key index for calling the typical load curve template of the corresponding industry.

[0030] Park-side energy data refers to the set of data obtained from the park's energy management system (EMS) that reflects the park's energy supply and consumption status. This includes the output coefficients of photovoltaic and wind power in each block, historical actual output data, meter data at key points, and the remaining power supply capacity of each block.

[0031] Among them, the remaining power supply capacity represents the upper limit of new power load that the target block can currently accept, which is a hard physical constraint for determining whether a project can be implemented.

[0032] Specifically, industry-side data is collected through the online data entry interface provided by the park's investment promotion management system. The system is set with a logic that distinguishes between required and optional fields. Planned annual electricity consumption, planned annual output value, and industry code are required fields, while historical load data are optional fields.

[0033] Data from the park is automatically retrieved through the standard data interface of the park's EMS system, and real-time snapshots of photovoltaic / wind power output coefficients, meter data at key points, and remaining power supply capacity in each block are periodically synchronized. After data collection, the system first executes the capacity hard constraint verification module, comparing the enterprise's planned annual electricity consumption with the remaining power supply capacity of the target block. If the former is greater than the latter, the system automatically triggers a "capacity insufficient" flag and returns a termination notification to the user, only sending items that pass the verification to the data queue for subsequent steps to be processed.

[0034] Step S20: Establish an enterprise load curve and calculate the total carbon emissions of the enterprise based on the enterprise-side energy data; establish a park green electricity curve based on the park-side energy data. It should be noted that, in this embodiment, the enterprise load curve refers to a normalized time series curve with time as the horizontal axis and enterprise power consumption as the vertical axis, which describes the distribution pattern and fluctuation pattern of enterprise power load over a period of time.

[0035] Total corporate carbon emissions refer to the estimated implicit carbon emissions at the corporate level, which are obtained by multiplying the planned annual electricity consumption reported by the enterprise by the industrial park's baseline carbon intensity.

[0036] The park's green electricity curve is a time series curve with time as the horizontal axis and green electricity output as the vertical axis. It reflects the output fluctuation characteristics of renewable energy sources such as photovoltaic or wind power in the park during a typical day and is a time series portrait of the green electricity supply side.

[0037] Understandably, establishing enterprise load curves and park green electricity curves makes the time-series characteristics that were originally hidden behind the annual electricity consumption figures explicit, thus making subsequent time-series matching analysis possible.

[0038] Specifically, the system first checks whether the enterprise has submitted historical load data. If it has, it directly performs normalization processing to generate a load curve and simultaneously updates its industry template library with the curve using an exponentially weighted moving average. If it has not submitted, it retrieves a substitute curve from the preset industry typical daily load curve template library based on the enterprise's industry code and adds a confidence level label to identify it.

[0039] The total carbon emissions of enterprises are calculated using the direct formula method: Estimated carbon emissions = Planned annual electricity consumption × Park baseline carbon intensity, where the park baseline carbon intensity is taken from the previous year's average power grid emission factor or the park's measured value.

[0040] The green electricity curve of the park is established by the system extracting the historical actual output data of photovoltaic or wind power in the target block where the project is to be located, selecting representative seasons (such as spring) and typical days, and generating a standardized 24-hour output curve after statistical averaging.

[0041] Step S30: Calculate the time-series matching degree between the enterprise load curve and the park green electricity curve; It should be noted that in this embodiment, the time-series matching degree is a quantitative indicator that measures the degree of fit between the enterprise's electricity consumption behavior and the park's green electricity supply in the time dimension. The value is a continuous value between 0 and 1, and the higher the score, the more similar the two are.

[0042] Specifically, the normalized load curve of the enterprise and the typical daily green power output curve of the park are used as two input sequences of the DTW (Dynamic Time Warping) algorithm to construct a distance matrix and solve for the minimum cumulative path distance through dynamic programming. The matching score is taken as the reciprocal of the DTW distance.

[0043] Furthermore, during the calculation process, the system embeds time-period weighting factors in the distance matrix construction stage. Specifically, when determining the point-to-point distance at each time point, the distance value during periods of high green electricity occurrence is multiplied by a discount factor less than 1, giving the alignment during those periods a higher contribution weight; the distance value during periods of low green electricity occurrence remains unchanged or is multiplied by an amplification factor greater than 1. The final output time-series matching score is fed into subsequent steps, serving both as a threshold criterion for hierarchical screening and as a key weighting parameter in the comprehensive scoring.

[0044] Step S40: Generate an investment attraction access list based on the time-series matching degree and the total carbon emissions of the enterprise.

[0045] It should be noted that the investment attraction access list is a structured decision-making recommendation document output by the system after a full-process analysis. Its contents include the name of the recommended project and its comprehensive score, the recommended location block, the recommended introduction time sequence, and the annual allocation plan.

[0046] In one feasible implementation, projects with a time-series matching degree lower than a preset threshold are screened out, and candidate projects are arranged from high to low according to the total carbon emissions of enterprises to obtain an investment access list.

[0047] This embodiment provides a method for evaluating investment attraction in zero-carbon industrial parks. First, by collecting energy data from both the enterprise and park sides, enterprise load curves and park green electricity curves are established, transforming the energy consumption behavior of enterprises and parks into quantifiable and comparable time series. Then, by calculating the time series matching degree of the two curves, the green electricity consumption capacity of enterprises in the time dimension is accurately measured numerically. Furthermore, an investment access list is generated based on the time series matching degree and the total carbon emissions of enterprises, ensuring that the introduced projects have low emissions in terms of carbon accounting and are in sync with the green electricity supply in terms of physical operation. This enables investment attraction evaluation of industrial parks based on the time series characteristics of green electricity output, thereby reducing carbon emissions.

[0048] Based on the first embodiment of this application, a second embodiment of this application is proposed. In this second embodiment, content that is the same as or similar to that in the first embodiment described above can be referred to the above description and will not be repeated hereafter. Based on this, please refer to... Figure 2 After step S10, the zero-carbon industrial park investment evaluation method further includes steps S01~S02: Step S01: Compare the planned annual electricity consumption in the enterprise-side energy data with the remaining power supply capacity in the park-side energy data; It should be noted that in this embodiment, the planned annual electricity consumption refers to the total amount of electricity that the prospective enterprise expects to consume each year after settling in, as filled in by itself during the investment application stage, and is usually measured in megawatt-hours (MWh).

[0049] Remaining power supply capacity refers to the upper limit of additional power supply capacity that the target block of the park can still carry on top of the load already connected at the current moment. It is usually measured in megawatts (MW) or converted to annual power supply capacity (MWh / year).

[0050] Specifically, after collecting the two types of data, the remaining power supply capacity field value of the target block is immediately extracted from the energy data dataset of the park side, and the planned annual electricity consumption field value reported by the enterprise is extracted from the energy data of the enterprise side. The two are then passed into the capacity verification function for comparison.

[0051] Step S02: If the planned annual electricity consumption is not greater than the remaining power supply capacity, then the following steps are performed: establish an enterprise load curve based on the enterprise-side energy data and estimate the total carbon emissions of the enterprise; establish a park green electricity curve based on the park-side energy data.

[0052] The verification function first standardizes the units, converting the remaining power supply capacity from power units to annual available power, calculated as: Annual available power = Remaining power supply capacity (MW) × Annual utilization hours coefficient. Then, it performs a numerical comparison, with the following branch logic: If the enterprise's planned annual power consumption is less than or equal to the target block's annual available power, a verification pass flag is returned, and the enterprise's data packet is moved from the pending verification queue to the input queue of the next processing stage; if it is greater, a "Capacity Insufficient - Terminate Assessment" flag is directly written to the enterprise's assessment status field, and a termination notification and specific data difference are returned to the user's front end. The enterprise will no longer participate in any subsequent calculations.

[0053] In this embodiment, a hard capacity constraint verification is performed by comparing the planned annual electricity consumption in the enterprise-side energy data with the remaining power supply capacity in the park-side energy data. Before any complex computing resources are invested, projects that do not meet the physical access conditions are accurately eliminated, ensuring that subsequent carbon efficiency assessment and time series matching analysis only apply to candidate projects that are physically feasible.

[0054] Based on the first and / or second embodiments of this application, a third embodiment of this application is proposed. In this third embodiment, content that is the same as or similar to the above embodiments can be referred to the above description, and will not be repeated hereafter.

[0055] In this embodiment, step S20, which establishes the enterprise load curve and calculates the total carbon emissions of the enterprise based on the enterprise-side energy data, may include steps S21 to S27: Step S21: Verify whether historical load data exists in the enterprise-side energy data; It should be noted that, in this embodiment, historical load data refers to the actual measurement data sequence recorded by the electricity metering device at fixed time intervals during the existing production and operation of the prospective enterprise, reflecting the change of the enterprise's power consumption over time in the past period.

[0056] Specifically, a data structure probing function receives an incoming enterprise data object as an input parameter. First, it parses the key-value list of the object and uses string matching or JSON path query to search for the existence of a field with the key name "historical_load_data" or an equivalent identifier.

[0057] If the field is retrieved, further checks are performed to verify whether its value is an array type with a length greater than zero, whether each element in the array contains two subfields: timestamp and power value, and whether the sampling interval of the timestamp is within a preset reasonable range. If all the above checks pass, the function returns a boolean value True to indicate the existence of valid historical load data; if any condition is not met or the field is missing, it returns a boolean value False to indicate that no usable historical load data exists.

[0058] In one optional implementation, the system not only verifies the existence of historical load data but also further verifies whether its data quality meets the minimum usability standards. Specifically, after detecting that the historical load data field is not empty, the system initiates a data quality assessment submodule to perform multi-dimensional verification on the time series of load data: First, it verifies the time coverage integrity of the data, requiring that the data record span is not less than one complete production cycle (e.g., 30 consecutive days), and the missing rate of valid data points does not exceed 15%, thereby excluding low-quality samples that only provide a few days of randomly sampled data; Second, it verifies the time resolution of the data, requiring that the sampling interval is not less than 1 hour, and data with excessively coarse sampling intervals (e.g., one point every 6 hours) is judged as unable to accurately depict the intraday load fluctuation pattern and is not adopted; Third, it verifies the numerical rationality of the data, checking for physically infeasible anomalies such as several consecutive hours of zero load but the enterprise claims continuous production, or instantaneous power surges exceeding the rated capacity of the equipment.

[0059] If all three checks pass, the historical load data is deemed valid and enters the normalization processing branch. If any check fails, the system records the specific reason for the substandard data quality in the enterprise assessment log and automatically transfers the enterprise to the industry template replacement branch, treating it as if it did not provide valid historical load data processing. At the same time, the confidence label is marked "data quality substandard - downgraded" for reference in subsequent comprehensive decision-making.

[0060] Step S22: If the historical load data exists in the enterprise-side energy data, then the historical load data is normalized to obtain a normalized load curve. Specifically, historical load data is read and the entire array `p_raw` is traversed to find the maximum power value `max_power`. Then, the array is traversed a second time, and for each time point, a division operation `p_norm = p_raw / max_power` is performed, with the result stored in a new array. After processing, a normalized array corresponding one-to-one with the timestamps of the original array is generated. This normalized array is combined with the timestamp vector to form a normalized load curve object. This object's data structure contains two equal-length arrays: a time axis array and a normalized load coefficient array. This object is then marked as originating from "measured data" and passed to the next processing step.

[0061] In one feasible implementation, the system first filters outliers from the historical load data sequence, removing outliers exceeding 1.5 times the interquartile range above and below the quartiles. This avoids interference from transient power spikes caused by occasional equipment startup shocks or short shutdowns on the normalized reference. Subsequently, the system extracts the 95th quantile of the filtered sequence as the normalized reference value, rather than directly using the maximum value. This fine-tuning ensures that the normalized load curve better reflects the actual power consumption pattern during steady-state production, rather than being dominated by individual extreme moments.

[0062] Furthermore, feature extraction is performed on the normalized curve to calculate three morphological indicators: peak concentration, valley depth, and peak-to-valley ratio. Based on these indicators, it is determined whether the enterprise belongs to a daytime concentrated, nighttime concentrated, or balanced continuous power consumption pattern, and this pattern label is appended to the normalized load curve metadata. This pattern label provides auxiliary information in subsequent time-series matching calculations—for example, daytime concentrated enterprises naturally have a morphological advantage in matching photovoltaic output curves, and the DTW algorithm can appropriately reduce the time scaling penalty coefficient when processing this curve, making the matching result more consistent with actual physical logic. This extended implementation method upgrades normalization from merely numerical scaling to a comprehensive preprocessing workflow that combines data cleaning, benchmark optimization, and pattern recognition, ensuring that the output normalized load curve is more morphologically stable and semantically richer.

[0063] Step S23: The normalized load curve is fused with the preset industry load template using an exponentially weighted moving average method to obtain the enterprise load curve; Specifically, the system receives a normalized load curve and an industry load template retrieved from an industry template library by industry code as input. The time axes of both are first aligned to a uniform 24-hour time series. If the sampling rates differ, linear interpolation is used for resampling. After alignment, EWMA (Exponentially Weighted Moving Average) is calculated for each of the 24 time points to obtain the enterprise load curve. The specific calculation formula is as follows: template_new[t]=α×curve_actual[t]+(1-α)×template_old[t] Where template_new[t] represents the normalized load value of the updated industry load template at time t, α represents the decay factor of the exponentially weighted moving average, curve_actual[t] represents the normalized load value in the normalized load curve at time t, and template_old[t] represents the normalized load value of the industry load template before the update at time t.

[0064] After the calculation is complete, the new template sequence is written back to the industry template library to overwrite the original old template records, along with updated metadata, including the last update timestamp, the cumulative number of samples participating in the fusion, and the template version number. This fused curve is also passed downstream as the current enterprise load curve, and its data source field is marked as "Measured Fusion Curve".

[0065] Step S24: If the historical load data is not found in the enterprise-side energy data, then determine the enterprise industry code from the enterprise-side energy data; If the historical load data is not found in the enterprise's energy data, the system accesses the "industry_code" field of the enterprise data object and reads its value. A format validation subroutine is then executed: it checks whether the field value is not empty, whether the length conforms to the encoding rules of standard industry codes (usually 1 to 5 characters), and whether a corresponding record exists in the system's pre-loaded industry code dictionary. If the validation passes, the industry code is used as the search key and passed to the next template retrieval step. If the industry code is empty or an invalid value, the system uses the default industry code as a fallback category and sets a special marker to indicate that the reliability of the enterprise's industry classification is questionable in subsequent operations. This marker is passed to the final confidence label.

[0066] Step S25: Retrieve the industry load template according to the enterprise industry code, set a confidence label for the industry load template, and use the industry load template with the confidence label as the enterprise load curve; Industry load templates refer to normalized time series curves that are pre-built and continuously updated, describing the typical daily electricity load distribution patterns of enterprises in specific industries. The initial version of this template library is derived from typical load curves statistically analyzed from various industries in publicly available energy databases, and it is continuously iterated and optimized based on actual data feedback from enterprises located in the park.

[0067] The confidence level label is a metadata tag that the system attaches to the industry load template to indicate to subsequent decision-making processes how reliable the template is as a substitute for real data.

[0068] Specifically, the system uses the industry code as the search key to perform exact match queries in a pre-built and persistently stored industry load template library. The industry load template library is typically stored in the system as a key-value database (such as Redis or JSON fields in a relational database), where the key is the industry code and the value is a structure containing an array of 24-hour normalized load values ​​and metadata fields.

[0069] After a match is found, the system reads the "cumulative update count" and "last update time" fields from the template's metadata and calculates the confidence level according to preset rules: "high confidence" is defined as more than 5 cumulative updates and the last update within one year; "medium confidence" is defined as 1 to 5 cumulative updates; and "low confidence" is defined as 0 cumulative updates (i.e., still the initial public data template). After writing the confidence level label into the template object's metadata fields, the system sets the data source marker for this template curve to "industry template - confidence level X," packages it into an enterprise load curve object, and outputs it to the downstream matching degree calculation module.

[0070] Step S26: Extract the planned annual electricity consumption from the enterprise-side energy data, and extract the park baseline carbon intensity from the park-side energy data; The planned annual electricity consumption is the total annual electricity consumption that enterprises report, expressed in megawatt-hours (MWH). It is a quantitative expression of the scale of enterprise electricity consumption.

[0071] The baseline carbon intensity of an industrial park refers to the average carbon dioxide emissions generated per megawatt-hour (MWh) of electricity supplied by the power grid in the park's area, expressed in tons of carbon dioxide per MWh (tCO2 / MWh). This value is typically determined based on the latest annual average emission factor of the power grid published by the national or provincial environmental protection authorities, but can also be customized based on the park's actual power supply structure and power purchase mix.

[0072] The system reads the planned annual electricity consumption from the enterprise data object and verifies its numerical type and the validity of positive values. It also reads the baseline carbon intensity field value from the park's energy management system configuration parameters. This field is manually updated annually by park energy management personnel based on the latest regional power grid emission factor data, or automatically synchronized through an interface with the provincial carbon management platform.

[0073] Step S27: Multiply the planned annual electricity consumption by the baseline carbon intensity of the park to obtain the total carbon emissions of the enterprise.

[0074] Specifically, a simple multiplication function is called to multiply the planned annual electricity consumption by the baseline carbon intensity of the industrial park to obtain the total carbon emissions of the enterprise.

[0075] In one feasible implementation, the system first obtains the typical daily time-of-use carbon emission factor curve of the power grid in the area where the park is located from the park's EMS or the provincial power grid emission data platform. This curve reflects the differences in the power structure of the power grid at different times—for example, the overall emission factor of the power grid is lower during the midday peak photovoltaic power generation period, while the emission factor is higher during the evening peak thermal power generation period.

[0076] Furthermore, the normalized load curve of the enterprise is read and restored to the time-of-use electricity consumption distribution expressed in absolute power value. The electricity consumption of each time period is multiplied by the time-of-use grid carbon emission factor corresponding to that time period and then summed to obtain a more accurate time-of-use weighted carbon emission estimate.

[0077] Furthermore, step S20, which establishes the park's green electricity curve based on the park-side energy data, may also include steps A21 to A22: Step A21: Determine the type of green power facility based on the energy data from the park side, and select a typical time period based on the type of green power facility; Green power facility types refer to the technical classification of clean energy power generation facilities that have been built and put into operation within the target area of ​​the park, mainly including photovoltaic power generation systems, wind power generation systems, and hybrid combinations of the two.

[0078] Typical time periods refer to the time periods that best represent the output characteristics of this type of green power facility. They are usually selected from the seasonal and intraday time segments of the year when the facility's output is most stable and closest to its design typical output curve. For example, for photovoltaic facilities, 6:00 to 18:00 in sunny spring weather can be selected as the typical time period.

[0079] The system accesses the new energy facility configuration dataset associated with the target block where the project is to be located in the park's energy data, and determines the type of green power facility by traversing and reading the dataset.

[0080] Furthermore, based on the type of facility read, the system triggers the corresponding typical time period selection strategy: if the type is photovoltaic, then the typical day is selected by default on a sunny day in spring (March-May), and the time window is set to 6:00 to 18:00; if the type is wind power, then the season with the wind speed closest to the average wind speed throughout the year is selected as a reference, and the time window is set to 24 hours a day; if the type is hybrid, then photovoltaic and wind power modules are processed separately and then superimposed.

[0081] Step A22: Establish the park's green electricity curve with the typical time period as the horizontal axis.

[0082] The system queries the park's historical database for green energy output records that match typical time periods. Query criteria include: facility identifier equal to the target block's new energy asset number, timestamp falling within the typical time window, and data quality marked as valid. The query returns a result set consisting of multiple sampling times and corresponding output power arrays. The system aligns these arrays by time and performs statistical calculations, typically using median aggregation to reduce interference from extreme weather events, generating an output curve containing several time sampling points (e.g., 24 whole hours).

[0083] The curve was then encapsulated into a structured data object containing an array of time axes and an array of output values. Its fields include the block number to which the curve belongs, facility type, typical time period identifier, and output value sequence, serving as a standardized output of the park's green electricity curve.

[0084] In one feasible implementation, the system first extracts hourly power output data for the past three full natural years from the park's EMS historical database based on the determined facility type. The data is then cross-grouped by seasonal dimension (spring, summer, autumn, winter) and weather type dimension (sunny, cloudy, rainy). The power output data within each group is aligned by time and the median power output value is calculated to generate the typical green electricity curve corresponding to that group.

[0085] When the facility type is wind power, since wind power output does not have the diurnal periodicity of photovoltaic power, the system further introduces a wind speed persistence factor to filter the raw data. Only data from stable periods with wind speeds between the cut-in wind speed and the rated wind speed, and with a fluctuation standard deviation less than a preset threshold, are retained for constructing typical curves to eliminate interference from non-representative operating conditions such as extreme gusts. Based on this, the system merges all grouped typical curves to calculate the output value distribution at each time moment, extracts the 10th percentile, median, and 90th percentile at each time moment, and plots a probability interval output band—the median curve—enclosed by the three curves, as the benchmark matching curve for DTW distance calculation.

[0086] In this embodiment, a curve representation system that is completely symmetrical in time dimension is constructed for both supply and demand sides. On the demand side, a load curve is established with measured data as the priority and industry templates as a fallback through a branch judgment mechanism. EWMA fusion enables the template to have self-learning and evolution capabilities, while a simplified formula is used to estimate total carbon emissions. On the supply side, typical time periods are selected according to the type of green energy facilities, and standardized output curves are established. Both curves are presented in a normalized form at a unified time granularity, eliminating the interference of absolute magnitude on matching analysis. This allows subsequent time-series matching degree calculations to focus on the essential fit between enterprise electricity consumption and green energy output in terms of shape and rhythm, providing a data foundation for investment promotion assessment of zero-carbon industrial parks.

[0087] Based on the above embodiments of this application, a fourth embodiment of this application is proposed. In this fourth embodiment, content that is the same as or similar to the above embodiments can be referred to the above description, and will not be repeated hereafter.

[0088] In this embodiment, step S30, which calculates the time-series matching degree of the enterprise load curve and the park green electricity curve, may include steps S31 to S32: Step S31: Calculate the dynamic time-normalized distance between the enterprise load curve and the park green electricity curve; Dynamic time warping distance is a quantitative indicator that measures the similarity in shape between two time series curves, calculated based on the dynamic time warping algorithm.

[0089] It's important to note that unlike the traditional Euclidean distance, which corresponds point-to-point, DTW allows a point on one curve to be aligned with multiple consecutive points on another curve. By locally stretching or compressing the time axis, it finds the alignment path that minimizes the total cumulative distance between the two curves. This minimum cumulative distance is the DTW distance; the smaller the value, the more similar the two curves are in shape.

[0090] The system uses the normalized arrays of enterprise load curves and park green energy curves as the two input matrices for the DTW algorithm. First, a 24×24 cost matrix is ​​constructed, where each matrix element (i,j) is the absolute value of the difference between the normalized value of the enterprise load curve at time i and the normalized value of the green energy curve at time j. Then, a dynamic programming recursive formula is used to calculate the cumulative distance matrix, as follows: D(i,j)=cost(i,j)+min(D(i-1,j),D(i,j-1),D(i-1,j-1)) The min function takes the minimum value among the three. D(i,j) represents the value of the element in the i-th row and j-th column of the cumulative distance matrix, that is, the minimum total distance accumulated from the starting point of the two curves to the pairing point of the enterprise load curve at time i and the green power curve at time j. cost(i,j) represents the value of the element in the i-th row and j-th column of the cost matrix, that is, the absolute value of the difference between the normalized load value of the enterprise load curve at time i and the normalized output value of the park green power curve at time j.

[0091] After the recursion is completed, the value of the last element D(24,24) of the matrix is ​​the DTW distance between the two curves.

[0092] Step S32: The dynamic time warping distance is weighted using a preset time period weighting factor to obtain the time series matching degree.

[0093] The time period weighting factor is a set of preset weight coefficient vectors that correspond one-to-one with each time period of the 24 hours of a day. Its value reflects the difference in importance of different time periods in the assessment of green energy consumption.

[0094] The core design logic of the time period weighting factor is to assign higher weight values ​​to the peak green electricity generation periods (such as the peak photovoltaic output period from 10:00 to 14:00) and lower weight values ​​to the low green electricity generation periods or periods with zero output (such as the period from 23:00 to 5:00 the next day), thereby forming a differentiated time period importance evaluation system.

[0095] After completing the DTW distance calculation, the system does not directly weight the final distance. Instead, it embeds the role of time period weighting factors during the cost matrix construction stage of the DTW algorithm. Specifically, when calculating each element cost(i,j) of the cost matrix, the system obtains the preset time period weight value w_j corresponding to time j (the time corresponding to the green electricity curve in the park), and multiplies the original point-to-point distance by this weight value.

[0096] For example, during periods of high green electricity generation, the weight value w_j is greater than 1, which amplifies the point-to-point distance in the cumulative distance calculation, causing the optimal alignment path of DTW to tilt towards the high-weight periods. For off-peak periods, the weight value is set to a normal value less than or equal to 1. After the DTW cumulative distance is calculated using the weighted cost matrix, the reciprocal of the final weighted DTW distance is taken and mapped to a matching score in the interval of 0 to 1, which is used as the final output value of the time-series matching degree.

[0097] In this embodiment, by endowing the matching degree calculation with the ability to identify the potential for flexible adjustment of enterprise electricity consumption time, and by embedding the importance difference of green electricity peak periods into the algorithm layer through time period weight factors, the matching degree score itself carries the policy guidance signal of "encouraging electricity consumption during the right time", guiding enterprises to optimize production scheduling from the source of investment promotion to improve the overall green electricity consumption ratio of the park.

[0098] Based on the above embodiments of this application, a fourth embodiment of this application is proposed. In this fourth embodiment, content that is the same as or similar to the above embodiments can be referred to the above description, and will not be repeated hereafter.

[0099] Based on this, please refer to Figure 3 , Figure 3 The flowchart of the fourth embodiment provided in this application is as follows: Figure 3 As shown, step S40, which generates the investment attraction access list based on the time-series matching degree and the total carbon emissions of the enterprises, may include steps S41 to S43: Step S41: Extract the planned annual output value from the enterprise-side energy data, divide the total carbon emissions of the enterprise by the planned annual output value, and obtain the carbon emissions per unit output value. The planned annual output value refers to the total monetary value of products or services that prospective enterprises are expected to create each year after settling in, as filled in by themselves during the investment promotion application stage. It is usually measured in tens of thousands of yuan.

[0100] Carbon emissions per unit of output value refer to the amount of carbon dioxide emitted for every 10,000 yuan of output value created. The unit is tons of carbon dioxide per 10,000 yuan. The calculation formula is: the enterprise's estimated total carbon emissions divided by the planned annual output value.

[0101] The system reads the planned annual output value from the enterprise's energy data object and calls the carbon efficiency calculation function to perform a division operation to obtain the carbon emissions per unit output value. After the calculation is completed, the system performs a reasonableness check, comparing the calculation result with the preset industry carbon efficiency benchmark range. If it deviates significantly from the range, it is marked as data anomaly, indicating that the enterprise may have overstated its output value or distorted its electricity consumption data.

[0102] Step S42: Establish a two-dimensional evaluation coordinate system with the carbon emissions per unit output value as the horizontal axis and the time-series matching degree as the vertical axis; The two-dimensional assessment coordinate system is a Cartesian coordinate system constructed with carbon emissions per unit of output as the horizontal axis (X-axis) and time-series matching degree as the vertical axis (Y-axis). The horizontal axis represents carbon emissions per unit of output from low to high from left to right, with the left side representing excellent carbon efficiency and the right side representing poor carbon efficiency; the vertical axis represents time-series matching degree from low to high from bottom to top, with the top representing excellent green electricity consumption matching and the bottom representing poor green electricity consumption matching.

[0103] Understandably, by projecting each candidate enterprise's carbon emissions per unit of output and its time-series matching degree onto this coordinate plane, all enterprises are visually distributed across four quadrants. The first quadrant (top right) represents "double-excellent" projects with high matching degree and low carbon emissions; the second quadrant (top left) represents "uneven" projects with high matching degree and high carbon emissions; the third quadrant (bottom left) represents "double-poor" projects with low matching degree and high carbon emissions; and the fourth quadrant (bottom right) represents another type of "uneven" project with low matching degree and low carbon emissions.

[0104] Specifically, the system creates a two-dimensional canvas object, setting the horizontal axis label to "Carbon Emissions per Unit Output (tCO2 / 10,000 RMB)" and the vertical axis label to "Time-Series Matching Degree (0-1)". The scale range of the horizontal axis is adaptively determined based on the minimum and maximum values ​​of carbon emissions per unit output of all enterprises to be evaluated in the current assessment batch, with a 10% margin above and below to avoid data points touching the edges. The vertical axis is fixed at a scale range of 0 to 1. The system adds two auxiliary reference lines as the basis for quadrant division: a vertical reference line in the vertical direction is set as the average value of carbon emissions per unit output of the projects currently located in the park, and a horizontal reference line in the horizontal direction is set as the matching degree threshold of 0.4. These two reference lines divide the coordinate system into four quadrant regions, and assign a preset label text to each quadrant region: the first quadrant is labeled "Priority Admission Zone", the second quadrant is labeled "Conditional Admission Zone", the third quadrant is labeled "Careful Observation Zone", and the fourth quadrant is labeled "Flexible Guidance Zone". The system then iterates through all the companies to be evaluated, taking their unit output carbon emissions and matching degree values ​​as XY coordinate pairs, plotting scatter points in the coordinate system and labeling the company names, thus completing the construction of a two-dimensional evaluation matrix.

[0105] Step S43: Select candidate investment projects based on the two-dimensional evaluation coordinate system, and perform time-series analysis on the candidate investment projects to obtain an investment access list.

[0106] Candidate investment projects refer to a subset of projects selected from all projects to be evaluated in the two-dimensional evaluation coordinate system, based on preset screening rules, that are eligible to enter the next step of time-series deduction and final access competition.

[0107] In one feasible implementation, step S43 may include steps S431 to S433: Step S431: Include the projects to be evaluated in the first quadrant of the two-dimensional evaluation coordinate system into the candidate investment projects; The system iterates through the data points of all projects to be evaluated in the generated two-dimensional evaluation coordinate system and directly includes projects in the first quadrant into the candidate investment projects.

[0108] Step S432: Set the constraint set for the candidate investment projects, and perform multiple rounds of rolling time-series simulation on the candidate investment projects based on the constraint set to obtain the time-series simulation path; The constraint set is a set of rigid boundary conditions that must be followed during the project introduction process, jointly defined by the park's zero-carbon construction standards and the carrying capacity of physical infrastructure. Specifically, it includes the annual carbon budget cap constraint (the total amount of carbon emission allowances that can be allocated each year), block capacity ramp-up constraint (the upper limit of the annual power supply capacity that can be added to each block), green electricity consumption guarantee constraint (the overall green electricity consumption ratio of the park after the introduction of projects each year must not be less than 50%), industry agglomeration constraint (projects in the same industry should preferably be concentrated in adjacent years), and fiscal matching pace constraint (the total annual investment in projects should not exceed the carrying capacity multiple of the park's annual infrastructure matching fund budget).

[0109] The system first loads the parameter values ​​of the constraint set from the park's configuration file and instantiates them as constraint objects. Then, it initializes an empty time-series trajectory data structure to store the list of approved projects for each year, along with their corresponding carbon budget balance, capacity balance, and green electricity consumption ratio snapshots. The core loop of the simulation uses an annual counter `year` incrementing from 1, and performs the following operations in each annual iteration: Projects are selected from the candidate project queue according to a preset sorting rule (e.g., carbon emissions per unit of output in ascending order). The system then checks whether the remaining capacity, remaining carbon budget, and project dependency conditions of the assigned block for the current year are simultaneously met. If met, the project is added to the annual admission list by the year in question, its allocated quota is deducted from the corresponding budget and capacity, and it is removed from the queue. If not met, the project remains in the queue, incremented by the year in question, awaiting verification in the next year. The loop terminates when the candidate queue is empty or no projects can be scheduled for several consecutive years. After the simulation, the system packages the admission records and project delay trajectories for each year into a time-series simulation path object and saves it to the evaluation context.

[0110] Step S433: Generate the investment access list based on the time-series deduction path and the candidate investment projects.

[0111] The system reads the time-series projection path object and candidate project list from the assessment context and creates a blank investment attraction access list document template. For projects successfully admitted each year in the time-series projection path, the system extracts the complete assessment data of the project from the candidate project dictionary and fills in the fields of the list template accordingly: project name and code are written into the basic information column; the recommended introduction year is extracted from the projection path and written into the column; quadrant label and comprehensive score are read from the assessment data object and written into the column; the location block is read from the project's original data and written into the column; and indicators such as the green energy consumption ratio and carbon budget balance before and after introduction are calculated by taking a snapshot of the projection path and written into the impact assessment column. For projects postponed to the deferred pool, a supplementary list is created and attached to the main list, indicating the reasons for the postponement and possible introduction conditions. The final list is formatted and saved as a PDF or online report, and sent to decision-makers through the system front-end or email.

[0112] In this embodiment, a unified cross-industry carbon efficiency benchmark is constructed by carbon emissions per unit output value to eliminate the interference of enterprise size differences on the fairness of the evaluation; a two-dimensional evaluation coordinate system is established with carbon efficiency as the horizontal axis and matching degree as the vertical axis, and projects are automatically divided into four differentiated disposal quadrants, realizing the upgrade from single-indicator queuing to two-dimensional zoning decision-making; then, through multiple rounds of rolling time series simulation, the project introduction rhythm is simulated under multiple annual constraints such as carbon budget, block capacity and green electricity consumption, and finally a structured access list with complete reasoning path and indicator impact prediction is generated.

[0113] This application also provides a zero-carbon industrial park investment evaluation device; please refer to... Figure 4 The zero-carbon industrial park investment evaluation device includes: Data acquisition module 10 is used to collect energy data from both the enterprise side and the park side. Calculation module 20 is used to establish an enterprise load curve and calculate the total carbon emissions of the enterprise based on the enterprise-side energy data, and to establish a green electricity curve for the park based on the park-side energy data. The time-series matching module 30 is used to calculate the time-series matching degree between the enterprise load curve and the park green electricity curve; The list generation module 40 is used to generate an investment access list based on the time-series matching degree and the total carbon emissions of the enterprise.

[0114] The zero-carbon industrial park investment promotion evaluation device provided in this application, employing the zero-carbon industrial park investment promotion evaluation method described in the above embodiments, can solve the technical problems of zero-carbon industrial park investment promotion evaluation. Compared with the prior art, the beneficial effects of the zero-carbon industrial park investment promotion evaluation device provided in this application are the same as those of the zero-carbon industrial park investment promotion evaluation method provided in the above embodiments, and other technical features in the zero-carbon industrial park investment promotion evaluation device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0115] This application provides a zero-carbon industrial park investment promotion assessment device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the zero-carbon industrial park investment promotion assessment method in the above embodiment 1.

[0116] The following is for reference. Figure 5 The diagram illustrates a structural schematic suitable for implementing the zero-carbon industrial park investment promotion assessment device in the embodiments of this application. The zero-carbon industrial park investment promotion assessment device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The zero-carbon industrial park investment assessment equipment shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0117] like Figure 5As shown, the zero-carbon industrial park investment promotion assessment equipment may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory 1002 or the program loaded from the storage device 1003 into the random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the zero-carbon industrial park investment promotion assessment equipment. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the zero-carbon park investment assessment equipment to communicate wirelessly or wiredly with other equipment to exchange data. While the figure shows zero-carbon park investment assessment equipment with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0118] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0119] The zero-carbon industrial park investment promotion evaluation equipment provided in this application, employing the zero-carbon industrial park investment promotion evaluation method described in the above embodiments, can solve the technical problems of zero-carbon industrial park investment promotion evaluation. Compared with the prior art, the beneficial effects of the zero-carbon industrial park investment promotion evaluation equipment provided in this application are the same as those of the zero-carbon industrial park investment promotion evaluation method provided in the above embodiments, and other technical features of the zero-carbon industrial park investment promotion evaluation equipment are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0120] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0121] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0122] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the zero-carbon industrial park investment evaluation method in the above embodiments.

[0123] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0124] The aforementioned computer-readable storage medium may be included in the zero-carbon park investment promotion evaluation equipment; or it may exist independently and not be installed in the zero-carbon park investment promotion evaluation equipment.

[0125] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the zero-carbon industrial park investment promotion assessment equipment, the zero-carbon industrial park investment promotion assessment equipment: collects enterprise-side energy data and park-side energy data; establishes an enterprise load curve based on the enterprise-side energy data and calculates the total carbon emissions of the enterprise; establishes a park green electricity curve based on the park-side energy data; calculates the time-series matching degree between the enterprise load curve and the park green electricity curve; and generates an investment access list based on the time-series matching degree and the total carbon emissions of the enterprise.

[0126] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0127] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0128] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0129] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-described zero-carbon park investment promotion evaluation method, and can solve the technical problems of zero-carbon park investment promotion evaluation. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the zero-carbon park investment promotion evaluation method provided in the above embodiments, and will not be repeated here.

[0130] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the zero-carbon industrial park investment evaluation method described above.

[0131] The computer program product provided in this application can solve the technical problems of investment promotion evaluation in zero-carbon industrial parks. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the zero-carbon industrial park investment promotion evaluation method provided in the above embodiments, and will not be repeated here.

[0132] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for evaluating investment attraction in zero-carbon industrial parks, characterized in that, The zero-carbon industrial park investment evaluation method includes: Collect energy data from both the enterprise and industrial park sides; Establish enterprise load curves and calculate total carbon emissions based on enterprise-side energy data; establish park green electricity curves based on park-side energy data. Calculate the time-series matching degree between the enterprise load curve and the park green electricity curve; A list of companies eligible for investment is generated based on the time-series matching degree and the total carbon emissions of the enterprises.

2. The zero-carbon industrial park investment evaluation method as described in claim 1, characterized in that, Following the steps of collecting enterprise-side energy data and park-side energy data, the following also includes: Compare the planned annual electricity consumption in the enterprise-side energy data with the remaining power supply capacity in the park-side energy data; If the planned annual electricity consumption is not greater than the remaining power supply capacity, then the following steps are performed: establish an enterprise load curve based on the enterprise-side energy data and estimate the total carbon emissions of the enterprise; establish a park green electricity curve based on the park-side energy data.

3. The zero-carbon industrial park investment evaluation method as described in claim 1, characterized in that, The steps of establishing an enterprise load curve and calculating the total carbon emissions of the enterprise based on the enterprise-side energy data include: Verify that historical load data exists in the enterprise-side energy data; If the historical load data exists in the enterprise-side energy data, then the historical load data is normalized to obtain a normalized load curve. The normalized load curve is fused with a preset industry load template using an exponentially weighted moving average method to obtain the enterprise load curve; If the historical load data is not found in the enterprise-side energy data, the enterprise industry code is determined from the enterprise-side energy data. The industry load template is retrieved based on the enterprise industry code, a confidence label is set for the industry load template, and the industry load template with the confidence label is used as the enterprise load curve. The planned annual electricity consumption is extracted from the enterprise-side energy data, and the baseline carbon intensity of the park is extracted from the park-side energy data. The total carbon emissions of the enterprise are obtained by multiplying the planned annual electricity consumption by the baseline carbon intensity of the park.

4. The zero-carbon industrial park investment evaluation method as described in claim 1, characterized in that, The step of establishing the park's green electricity curve based on the park-side energy data includes: The type of green power facility is determined based on the energy data from the park, and a typical time period is selected based on the type of green power facility. A green electricity curve for the park is established with the typical time period as the horizontal axis.

5. The zero-carbon industrial park investment evaluation method as described in claim 1, characterized in that, The step of calculating the time-series matching degree of the enterprise load curve and the park green electricity curve includes: Calculate the dynamic time-normalized distance between the enterprise load curve and the park green electricity curve; The time-series matching degree is obtained by weighting the dynamic time warping distance using a preset time period weighting factor.

6. The zero-carbon industrial park investment evaluation method as described in claim 1, characterized in that, The steps for generating the investment access list based on the time-series matching degree and the total carbon emissions of enterprises include: Extract the planned annual output value from the enterprise's energy data, and divide the enterprise's total carbon emissions by the planned annual output value to obtain the carbon emissions per unit output value. A two-dimensional evaluation coordinate system is established with the carbon emissions per unit output value as the horizontal axis and the time-series matching degree as the vertical axis. Based on the two-dimensional evaluation coordinate system, candidate investment projects are selected, and the time-series analysis of the candidate investment projects is performed to obtain an investment access list.

7. The zero-carbon industrial park investment evaluation method as described in claim 6, characterized in that, The steps of selecting candidate investment projects based on the two-dimensional evaluation coordinate system and performing time-series analysis on the candidate investment projects to obtain the investment access list include: The projects to be evaluated in the first quadrant of the two-dimensional evaluation coordinate system are included in the candidate investment projects; Set a set of constraints for the candidate investment projects, and perform multiple rounds of rolling time-series simulation on the candidate investment projects based on the set of constraints to obtain the time-series simulation path; The investment access list is generated based on the time-series deduction path and the candidate investment projects.

8. A zero-carbon industrial park investment evaluation device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the zero-carbon industrial park investment evaluation method as described in any one of claims 1 to 7.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the zero-carbon industrial park investment evaluation method as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the zero-carbon industrial park investment evaluation method as described in any one of claims 1 to 7.

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